The deployment of billions of Internet of Things (IoT) devices is driving unprecedented data generation at the network edge, demanding high computational power for real-time deep learning (DL) while raising serious concerns about energy consumption. While edge computing offers a viable paradigm for decentralized DL by preserving data privacy and reducing latency, the substantial energy costs of DL training and inference pose a major challenge for resource-constrained edge devices. This work provides a comprehensive review of state-of-the-art studies that address energy efficiency at the intersection of DL and edge computing. Moving beyond isolated solutions, we analyze the critical need for a co-design approach integrating hardware and software with adaptive resource management to build sustainable systems. The paper systematically examines hardware-level optimizations and software-level techniques for reducing energy consumption while maintaining model accuracy. Furthermore, it investigates how adaptive management of compute, memory, and communication resources is key to dynamic energy savings. Finally, the paper synthesizes recent trends, identifies emerging opportunities, and discusses open challenges, positioning hardware-software co-design as the most promising approach for achieving scalable and energy-efficient deep learning in edge computing.

With the widespread adoption of cloud-based services and Service-Oriented Computing, efficient service recommendation has become pivotal for optimizing service discovery and composition in large-scale ecosystems. While recent diffusion-based recommendation methods have achieved impressive results in service-oriented scenarios, existing approaches predominantly treat user-service interactions as isolated events, overlooking the potential of higher-order collaborative signals between users and services. Such signals, which encapsulate richer and more nuanced relationships, can be naturally captured using graph-based data structures. To address this limitation, we extend diffusion-based service recommendation methods to the graph domain by directly modeling user-service bipartite graphs with diffusion models. This enables better modeling of the higher-order connectivity inherent in complex interaction dynamics. However, this extension introduces two primary challenges: (1) Noise Heterogeneity, where interactions are influenced by various forms of continuous and discrete noise, and (2) Relation Explosion, referring to the high computational costs of processing large-scale graphs. To tackle these challenges, we propose a Graph-based Diffusion Model for Service Recommendation (GDMSR). To address noise heterogeneity, we introduce a multi-level noise corruption mechanism that integrates both continuous and discrete noise, effectively simulating real-world interaction complexities. To mitigate relation explosion, we design a user-active guided diffusion process that selectively focuses on the high-value edges and active users, reducing inference costs while preserving critical service-level dependencies. Extensive experiments on six real-world service datasets demonstrate that GDMSR consistently outperforms state-of-the-art methods, highlighting its effectiveness in capturing higher-order collaborative signals and improving service recommendation performance.

Software service discovery and recommendation help data scientists build scientific workflows - multi-step data analytics procedures - by automating the manual selection of services. Previous research shows that recommending chainable units of work (UoWs), rather than individual services, improves efficiency and reduces data shimming issues. However, UoW recommendation remains an NP-hard problem. To tackle this challenge, this study introduces a novel framework tailored to recommend UoWs in a goal-driven, context-aware manner, thereby facilitating workflow development. The framework is built around layered structure of software service social networks. At its foundation lies a service dependency network, where each edge represents a dependency between a two-service UoW within a specific context. The next layer abstracts each of these edges into a node, with new edges now representing three-service UoWs. This layering process continues iteratively, with subsequent layers capturing increasingly complex UoWs at higher levels of granularity. At high-order layers, UoW nodes are clustered based on their semantic embeddings, with each cluster represented by an intelligent agent. This approach transforms the workflow recommendation problem into a multi-agent collaboration task, where agents work together to identify high-level UoW groupings before refining selections by navigating down the layered structure for finer-grained recommendations. Experimental results over a real-world dataset confirm the effectiveness of the proposed framework in enhancing workflow composition efficiency.

Generative AI for automated glaucoma diagnostic report generation faces two predominant challenges: content redundancy in narrative outputs and inadequate highlighting of pathologically significant features including optic disc cupping, retinal nerve fiber layer defects, and visual field abnormalities. These limitations primarily stem from current multimodal architectures' insufficient capacity to extract discriminative structuraltextural patterns from fundus imaging data while maintaining precise semantic alignment with domain-specific terminology in comprehensive clinical reports. To overcome these constraints, we present the Dual-Attention Semantic Parallel-LSTM Network (DA-SPL), an advanced multimodal generation framework that synergistically processes both fundus imaging and supplementary visual inputs. DA-SPL employs an Encoder-Decoder structure augmented with the novel joint dual-attention mechanism in the encoder for cross-modal feature refinement, the parallelized LSTM decoder architecture for enhanced temporal-semantic consistency, and the specialized label enhancement module for accurate disease-relevant term generation. Rigorous evaluation on standard glaucoma datasets demonstrates DA-SPL's consistent superiority over state-of-the-art models across quantitative metrics. DA-SPL exhibits exceptional capability in extracting subtle pathological indicators from multimodal inputs while generating diagnostically precise reports that exhibit strong concordance with clinical expert annotations. Code is here: https://github.com/CH-YellowOrange/DA-SPL.

Time series forecasting techniques have significant value in domains such as industrial production, financial markets, and energy management. Accurate prediction of future time series is vital for decision making and operational optimization. However, existing methods often face two major difficulties, i.e., insufficient available training data and the challenging requirement for long-term predictions, where errors tend to accumulate over time. These limitations highlight the need for more reliable long-term forecasting methods. This work proposes a novel hybrid approach that combines VMD with attention-based transformer-style models to address these challenges. The VMD module decomposes raw sequences into simpler and more stable elements, reducing noise and irregular patterns. The transformer-style model captures long-range dependencies through its attention mechanism to identify meaningful long-term relationships in data. The proposed method is evaluated on multiple benchmark datasets, including ETT, ECL, and Traffic, which represent different real-world scenarios. The experimental result shows that the proposed method reaches higher prediction accuracy compared to existing methods across all datasets and prediction horizons, especially in long-term forecasting scenarios.

Time series forecasting is a fundamental task in many domains, such as finance, energy, and intelligent systems. It is increasingly important in modern computing environments, including cloud computing and distributed resource management. However, real-world time series often exhibit complex temporal dependencies, high volatility, and multi-scale nonlinear patterns, making accurate forecasting challenging. To address these issues, this work proposes SVBTformer, a novel and effective forecasting model that enhances the Transformer-based Informer architecture with structured temporal learning modules. Specifically, SVBTformer integrates Savitzky–Golay (SG) filtering for noise reduction and signal smoothing, followed by Variational Mode Decomposition (VMD) to extract multi-resolution temporal components. Then, an improved Informer network called BTformer is employed to enhance the modeling capability for time series and strengthen the extraction of temporal dependencies. This work extensively experiments on publicly available benchmarks spanning multiple domains, including the ETT dataset for electric power demand, foreign exchange rates, and meteorological measurements. The results demonstrate that SVBTformer consistently outperforms state-of-the-art models, such as Informer and Autoformer, across most evaluation metrics, delivering superior accuracy and robustness. These gains underscore SVBTformer’s strong generalization capability and suitability for deployment in various real-world time series applications.

With the rapid advancement and integration of Internet of Things technology into manufacturing, industrial workshops in computer, communication, and consumer electronics (3C) manufacturing are increasingly confronted with complex computational tasks during production. However, the limited hardware resources and computational capabilities of local devices often hinder efficient task execution. Computational offloading offers a viable solution by allowing complex computational tasks to be processed on either edge or cloud servers, enhancing the efficiency of computational task handling in production environments. A critical challenge lies in optimizing task offloading among local devices, edge servers, and cloud servers to maximize production efficiency while ensuring reasonable task scheduling. To address this challenge, this work proposes a flexible computational offloading strategy based on an edge-cloud architecture in a smartphone manufacturing workshop. First, a framework for edge-cloud workshop manufacturing is constructed, integrating various smartphone production devices. Based on the edge-cloud framework, a constrained optimization problem for computation offloading is formulated, using latency as the objective in industrial production settings. A time consumption model is employed to optimize computational time, and a novel scheduling strategy named Ivy-Genetic Evolution Algorithm (IGEA) is designed to solve the scheduling problem. The IGEA integrates genetic operators into the Ivy Algorithm to introduce a randomness strategy. Experimental results demonstrate that IGEA significantly outperforms state-of-the-art approaches in optimizing production efficiency.

Medical image segmentation challenges stem from complex lesion morphologies and real-time clinical needs. Here we present MAR-Net, a novel framework that integrates adaptive attention mechanisms, hierarchical contextual modeling, and efficient training strategies to address these challenges. The architecture employs a CBAM-based dual-attention module to dynamically enhance discriminative features while suppressing redundant information, improving lesion boundary localization. Cascaded dilated convolutions expand the receptive field for global context capture, complemented by a multi-scale decoder that integrates deep semantic and shallow spatial features. A multi-scale training strategy with hierarchical loss supervision optimizes model adaptability without compromising inference efficiency. Experimental validation on ISIC and CholecSeg8K datasets demonstrates MAR-Net’s superiority: it outperforms mainstream methods across segmentation accuracy, recall rate, and other metrics, achieving notable improvements for complex lesions of varying sizes. Notably, MAR-Net maintains high performance on both dermoscopic and laparoscopic images, showcasing its broad applicability. These results confirm MAR-Net as a robust medical segmentation solution, balancing precision and efficiency for clinical use.

The Dynamic Job Shop Scheduling Problem (DJSP), a critical challenge in 3C manufacturing, requires efficient resource allocation under dynamically changing production conditions where jobs arrive unpredictably. Traditional optimization methods struggle to provide scalable solutions due to the high computational cost of searching for optimal schedules in large and complex environments. To solve this problem, this work proposes the Dual-Graph convolutional networks assisted Dynamic Cooperative Hunting Optimizer (DG-DCHO), which integrates graph-convolutional networks (GCN) with metaheuristic optimization to generate high-quality schedules while significantly improving computational efficiency. GCN generator processes graph representations of the job-shop environment and captures complex dependencies among jobs and machines to construct high-quality initial schedules that serve as initial solutions for the optimization process. GCN evaluator estimates makespan values directly from schedule representations and replaces costly fitness evaluation that minimizes computational overhead and improves optimization speed. Dynamic Cooperative Hunting Optimizer (DCHO) serves as the base optimizer and generates scheduling solutions by balancing global exploration with local exploitation through an adaptive search strategy. Experimental results across various DJSP instances demonstrate that DG-DCHO consistently outperforms state-of-the-art scheduling algorithms by producing superior solutions while requiring fewer computational resources, making itself a scalable and effective framework for real-time dynamic scheduling in large-scale 3C manufacturing systems.

Water quality anomaly detection refers to the identification of abnormal changes in water parameters, which is crucial for ensuring environmental safety and preventing contamination events. With the growing volume of water environment sensing data and increasing demand for intelligent, transparent water quality management systems, achieving accurate, rapid, and interpretable anomaly detection has become a critical challenge in early warning systems. To tackle this challenge, this work proposes an anomaly detection model named Shapelet Temporal Evolution Graph Network (STEG), which constructs time-aware Shapelets and adopts graph attention networks to build Shapelets evolution graphs, learning multidimensional dynamic relationships within and between time segments. By incorporating both local and global temporal evolution factors, the approach ensures the interpretability of both the detection process and its resulting outputs. Experiments on two real-world datasets show that STEG outperforms state-of-the-art methods in terms of anomaly detection accuracy and generalization. Moreover, it provides clear and transparent reasoning for water quality anomaly detection.

Accurate estimated time of arrival (ETA) prediction for long vehicular trips remains challenging in intelligent transportation systems (ITS) due to heterogeneous traffic patterns and limited local data availability. While federated learning (FL) addresses privacy concerns by decentralizing data training, traditional FL frameworks often struggle with high computational costs and poor adaptability to multi-task scenarios. To overcome these limitations, this paper proposes a Lightweight Multi-task Federated Learning (LMFL) framework for efficient and privacy-preserving ETA prediction. LMFL integrates a novel SE-CIFG, combining a Squeeze-Excitation (SE) attention module to prioritize critical spatio-temporal features and a Coupled Input and Forget Gate (CIFG) to simplify long-term traffic dependency modeling. Additionally, LMFL employs a Federated Gradient Compression Algorithm (FedGCA) to reduce communication overhead between edge and cloud using adaptive thresholding and sparse tensor encoding. Real-world traffic simulation dataset demonstrates that LMFL achieves significantly higher predictive accuracy compared to existing methods, achieving an average 17.1% improvement in prediction precision while reducing training time by 4.1%.

Water quality prediction methods forecast the short- or long-term trends of its changes, providing proactive advice for preventing and controlling water pollution. Existing water quality prediction methods typically fail to capture water quality’s nonlinear characteristics accurately and only consider historical time series data. However, meteorology and other factors also significantly impact water quality indicators. Therefore, considering only historical data of water quality time series is not feasible. To solve this problem, this work proposes a hybrid water quality prediction model called CMLIP, which integrates convNeXt V2, multimodal bottleneck transformer, low-rank multimodal fusion, iTransformer, and PatchTST. CMLIP inputs water quality time series and meteorological remotely sensed rainfall images into a multimodal fusion module before prediction. Specifically, CMLIP integrates the model of ConvNeXt V2 to extract image features. Its multimodal fusion module combines a multimodal bottleneck transformer and the low-rank multimodal fusion to fuse the time series and images. Furthermore, CMLIP combines iTransformer and PatchTST to form an improved prediction module that realizes the prediction of fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMLIP when fusing meteorological data, achieves an average improvement of 17% in water quality forecasting accuracy compared to forecasts using only water quality time series. Moreover, CMLIP outperforms other state-of-the-art algorithms in both data fusion and prediction, with an average enhancement of 6% in fusion effectiveness and an average improvement of 22% in prediction accuracy.

Given the forthcoming emergence of 6G communication models, the integration of terrestrial and nonterrestrial infrastructures is receiving increasing attention due to its widespread reach and broadcasting/multicast functions. The utilization of edge computing in space-related applications is appealing. However, the issue of positioning satellite edge servers and deploying services has yet to be resolved. Besides, existing studies mainly concentrate on energy consumption and latency problems, often neglecting the user mobility and potential privacy leakage issues in a mobile edge computing (MEC) environment. Yet it is crucial to optimize computation offloading and resource allocation for satellite-terrestrial edge computing networks. This work designs an innovative architecture for collaborative computation among multiple mobile devices and MEC servers deployed in ground stations and satellites. Based on this architecture, we formulate a nonlinear integer optimization problem to minimize the total system energy consumption. The model integrates several complex real-life nonlinear constraints, including operator cost, edge servers’ computing capacity, storage capacity, resource and latency, and privacy ones. To tackle the problem, this work proposes an advanced hybrid algorithm named a slime mold algorithm with genetic operations and individual updates of grey wolf optimizer (SMG2). SMG2 optimizes user mobility and privacy protection while optimizing server and service placement to minimize total energy consumption. Simulation experiments demonstrate that SMG2 reduces energy consumption drastically over the state of the art.

Water quality forecasting is a time series analysis task involving estimating future water conditions, vital in environmental management and pollution control. However, existing time series analysis methods focus only on historical observational data, neglecting information from other modalities, leading to incomplete feature extraction and affecting forecasting accuracy and robustness. In addition, the complex spatial dependencies between water quality monitoring stations and the nonlinear fluctuations in water quality indicators caused by meteorological factors present additional challenges. This work proposes a Spatio-Temporal Multimodal Fusion architecture for long-term water quality forecasting, named STMF, to address these issues. It first captures spatio-temporal dependencies by integrating temporal features with upstream-downstream relationships among monitoring stations. Then, STMF further designs a Low-rank Cross-modal Interaction Fusion (LRCIF) method, which fuses spatio-temporal features with precipitation features from the remote sensing image, as an additional modality, effectively leveraging complementary information from multiple data sources to enhance the accuracy and stability of water quality forecasting. Experimental results on real-world water quality datasets demonstrate that the proposed STMF significantly outperforms existing state-of-the-art methods in prediction accuracy. In particular, for long-term forecasting tasks with a 192-step horizon, STMF improves MSE and MAE by 14% and 12%, respectively, compared to unimodal models. It further validates the effectiveness of the multimodal fusion strategy. Overall, STMF offers an effective solution for water quality monitoring and management.

The advent of smart manufacturing in Industry 4.0 signifies the era of connections. As a communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, an integration framework named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. QOMOU extracts information models of OPC UA servers into resource description framework triples and utilizes web ontology language for semantic enrichment and inference. Then, an Event Class Semantic Similarity Calculation (ECSSC) method is proposed for device type identification, enabling the classification of semantically heterogeneous OPC UA devices. The effectiveness of ECSSC is validated through queries with the RDF query language (SPARQL) protocol in Apache Jena. Experimental results demonstrate that ECSSC improves the accuracy of device identification by approximately 7% compared to benchmark device identification models. Specifically, compared with graph embedding-based methods, QOMOU’s query performance is approximately 13% higher, and its query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture.

In many fields, time series prediction is gaining more and more attention, e.g., air pollution, geological hazards, and network traffic prediction. Water quality prediction uses historical data to predict future water quality. However, it is difficult to learn a representation map from a time series that captures the trends and fluctuations to effectively remove noise from the time series data and investigate complex nonlinear relationships. To solve these problems, this work proposes a time series prediction model, called DPSGT for short, which integrates Dual Patch Savitsky–Golay filtering and Transformer. First, DPSGT adopts the SG filtering to decompose the time series data and reduce the noise interference to improve long–term prediction capabilities. Second, to tackle the limitation of temporal representation capability, DPSGT adopts dual patches to ravel temporal series into local and global patches, which can tackle local semantic information and enlarge the receptive field. Third, it utilizes a transformer mechanism to address the nonlinear problem of the water quality time series and improve the accuracy of the prediction. Two real-world datasets are utilized to evaluate the proposed DPSGT, and experiments prove that DPSGT improves root mean-square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 by 6%, 5%, 8%, and 7%, respectively, compared with other benchmark models.

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm calledGeneticSimulated-annealing-basedParticle swarm optimization withAuto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs.

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy.

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption.

ABSTRACT Background In recent years, Large Models (LMs) have been rapidly developed, including large language models, visual foundation models, and multimodal LMs. They are updated and iterated at a very fast pace. These LMs can accomplish many tasks, e.g., daily work assistant, intelligent customer service, and intelligent factory scheduling. Their development has contributed to various industries in human society. Aims The architectural flaws of LMs lead to several problems, including illusions and difficulty in locating errors, limiting their performance. Solving these problems properly can facilitate their further development. Methods This work first introduces the development of LMs and identifies their current problems, including data and energy consumption, catastrophic forgetting, reasoning ability, localization fault, and ethical problems. Then, potential solutions to these problems are provided, including increase data and computation capability, neural‐symbolic synergy, and data orientation to human pattern. Discussion This work discusses developing vertical domain LMs on top of some base LMs. In addition, this work introduces three typical real‐world applications of LMs, including autonomous driving, smart industrial productions, and intelligent medical assistance. Conclusion By embracing the advantages of LMs and solving their fundamental problems, many industries are expected to achieve promising prospects in the future.

Over the past decades of rapid development, the global water pollution problem became prominent. Accurate water quality prediction can detect the trend and anomaly of water quality changes in advance, thereby taking timely measures to avoid water quality problems. Traditional statistical methods for water quality prediction tend to fail to capture the complex relationship among multiple water quality variables. Deep learning models face a challenge to capture both temporal dependence and spatial correlation of the water quality series data. To solve the above problems, this work proposes an adaptive and dynamic graph fusion water quality prediction model based on a spatiotemporal attention mechanism namedSpatial-TemporalGraphFusionTransformer (STGFT). It integrates a spatial attention encoder, a temporal attention encoder, an adaptive dynamic adjacency matrix generator, and a multi-graph fusion layer. Among them, the first two are adopted to capture the spatial correlations and temporal characteristics among different water quality monitoring stations, respectively. The generator can produce adaptive and dynamic adjacency matrices to reflect potential spatial relationships in a river network. Experimental results with real-life water quality datasets reveal that the prediction accuracy of STGFT outperforms the existing state-of-the-art models. Note to Practitioners—This paper is motivated by the problem of long-term water quality prediction. The highly volatile water quality data and the nonlinear characteristics of the time series greatly affect the accuracy of the forecasting task. Existing approaches fail to simultaneously capture spatial correlations and temporal characteristics among different water quality monitoring stations, affecting the accuracy of water quality predictions. This work proposes a water quality prediction method that captures the spatial correlations and temporal characteristics among different water quality monitoring stations. Moreover, it produces adaptive and dynamic adjacency matrices to reflect potential spatial relationships in a river network. Experimental results from three real-world datasets show that this approach is feasible and obtains more accurate prediction results. Furthermore, this method can also be applied to other areas of time series prediction, including finance, traffic, and smart manufacturing.

Water quality prediction provides timely insights for addressing potential water environmental issues. Transformer-based models have been widely used in water quality prediction. However, the following challenges exist: 1) Noise in the time series of water quality causes nonlinear models to be overfit; 2) It is difficult to identify temporal correlations in complex time series data; and 3) Information utilization is limited in long-term prediction. This work introduces a large-scale water quality prediction model named SVD-Autoformer to address them. SVD-Autoformer combines aSavitzky-Golay (SG) filter,variational modedecomposition (VMD), anauto-correlation mechanism, and a deep decomposition architecture, which is achieved in the renovation of the transformer. First, the SG filter removes noise while retaining valuable data features. SVD-Autoformer employs the SG filter as a data preprocessing tool to reduce noise and prevent nonlinear models from overfitting. Second, VMD extracts major modes of the signals and their respective center frequencies, thus providing richer features for the prediction. Third, the deep decomposition architecture with embedded decomposition modules allows for gradual decomposition during the prediction process. SVD-Autoformer employs the architecture to extract more predictable components from complicated water quality time series for long-term forecasting. Finally, SVD-Autoformer applies the auto-correlation mechanism to capture the temporal dependence and enhance information utilization. Numerous experiments are conducted and the results demonstrate that SVD-Autoformer provides superior prediction accuracy over other advanced prediction methods with real-world datasets. Note to Practitioners—This paper explores the critical aspects of time series water quality prediction, aiming to provide valuable insights for engineers and decision-makers. Traditional water quality prediction methods primarily rely on linear time series approaches and suffer from high computational complexity when dealing with large-scale data. This study is motivated by the transformer architecture with highly parallel computing capability and innovatively proposes deep decomposition architecture to extract more predictable components. In practice, to handle massive data with low time complexity, we introduce an auto-correlation mechanism. We conduct experiments using real-world datasets to demonstrate that this method achieves superior water quality prediction accuracy. Additionally, the method has been deployed in a real-world water quality prediction platform. Our future work includes its applications to different real-world datasets arising from electric power, intelligent transportation, and meteorological rainfall prediction.

Sequential recommender systems (SRSs) aim to predict the subsequent content in which users may be interested based on their past usage history. Existing solutions on SRSs focus on modeling sequential characteristics of user-service interactions and achieve promising performance. However, they do not take full advantage of one key factor that usually influences user behaviors: the category of services. It is necessary yet challenging to leverage category information due to two significant reasons. Firstly, bundling relationships exist between services/categories, which is vital for the prediction of user behaviors but hard to mine and encode. Secondly, since interest preferences and category preferences are closely related, their dynamic evolution has to be studied simultaneously. To tackle the above challenges, we propose a novel Dual-channel Preference Extraction and Refinement Network (DPERN) to extract users' multi-faceted preferences toward more accurate recommendation. For the former challenge, we leverage the co-occurrence information of services and categories to represent their intrinsic relationships and then adopt the graph embedding method to jointly pre-train their embeddings. For the latter challenge, we design dual preference extractors, each leveraging both service and category information, to capture interest preferences and category preferences, respectively. Moreover, we devise a preference refinement network to model the interaction between two extracted preferences, to enhance preference representations. Experimental results on three public datasets have demonstrated the effectiveness of the proposed DPERN model.

Efficient and accurate identification of network anomalies is significant to network security systems. It is highly challenging to detect abnormal behaviors in the increasing network data accurately. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have unsatisfying detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. In this work, based on the improvement of variational autoencoders, stacked sparse shrink variational autoencoders (S3VAEs) are designed. In addition, anUnbalancedXGBoost classifier based onGenetic simulated annealing particle swarm optimization (UXG) is proposed. Finally, the feature extractor of S3VAEs is combined with the UXG classifier, and the anomaly detection model is obtained. Experimental results based on four real-life data sets demonstrate that the proposed anomaly detection model achieves higher classification accuracy and F1 than several state-of-the-art algorithms.

As increasingly more APIs are published on the Internet, effective API recommendation remains a challenge yet highly demanded for mashup developers. This paper formalizes API recommendation as an incremental context-aware recom-mendation problem starting from a set of descriptive words and a set of APIs selected to date, supported by a fine-grained mashup-oriented knowledge graph (MKG). In contrast to traditional knowledge graphs where nodes are coarse-grained entities, entity-and relationship-encapsulated features are extracted as first-class citizens in an MKG, so that implicit feature relationships can be turned into explicit structural relationships. Two models are trained to learn fine-grained API selection strategies through path type patterns in the MKG, starting from intended descriptions and APIs selected, respectively. Extensive experiments over real-world datasets have demonstrated the effectiveness of the method.

In recent years, distributed computing has wit-nessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and inter-dependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVI for achieving high-accuracy workload prediction in distributed computing systems. SVI employs the Savitzky-Golay filter and variational mode decomposition for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVI improves the prediction accuracy by 35.4% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVI enhances the prediction accuracy of workload, CPU, and memory by 62.5%, 65.6%, and 69.1 %, respectively.

Water quality prediction methods forecast the future short or long-term trends of its changes, providing proactive advice for water pollution prevention and control. Existing water quality prediction methods only consider the historical data of single-type or multi-type water quality. However, meteorology and other factors also have a significant impact on water quality indicators. Therefore, only considering the historical data of water quality is not feasible. Unlike existing studies, this work proposes a hybrid water quality prediction model called CMI to solve the above problem. Before prediction, CMI incorporates a multimodal fusion mechanism of water quality time series and remote sensing images of meteorological rainfall. Moreover, CMI integrates the model of ConvNeXt V2 and a multimodal bottleneck transformer to extract image features for fusing the time series and images. Furthermore, it utilizes an emerging model of iTransformer to realize prediction with the fused features. Experimental results with real-life water quality time series and remotely sensed rainfall images demonstrate that CMI outperforms other state-of-the-art fusion algorithms, and the water quality prediction accuracy with fused meteorological data is 13% higher on average than that with only water quality time series.

Many new IoT applications have emerged with the fast evolution of 5G and the Internet of Things (IoT). These applications place higher demands on network energy consumption and processing capabilities. Mobile edge computing (MEC) significantly enhances execution efficiency, while energy harvesting (EH) modules further augment the operational features of IoT devices. However, existing studies mainly concentrate on energy consumption and latency problems, often neglecting issues about user mobility and potential privacy leakage within the MEC environment. Therefore, optimizing computation offloading and resource allocation for MEC-enabled IoT networks is essential. This work proposes an innovative architecture with EH for collaborative computing between multiple mobile devices (MDs) and MEC servers. To tackle the problem, this work also proposes an advanced hybrid algorithm named Self-adaptive Bat Optimizer with Genetic operations and individual update of Grey wolf optimizer (SBG2). With SBG2, this work aims to minimize the energy consumption of MDs while providing user mobility and privacy protection. Simulation experiments show that SBG2 reduces energy consumption by 79.15%, 93.20%, and 89.58%, respectively, compared to the other three typical algorithms.

Mobile edge computing (MEC) emerges as a vital paradigm to support the increasing use of mobile users (MUs) with capabilities similar to cloud computing. While most research concentrates on MEC facilitated by terrestrial base stations (BSs), its applicability in scenarios such as disaster rescue and field operations is limited. Efforts have been made to explore MEC assisted by unmanned aerial vehicles (UAVs) with efficient scheduling algorithms. However, relying solely on UAVs for MEC has limitations, particularly for computation-intensive applications. This work proposes a hybrid MEC system lever-aging UAVs and BS. Multiple UAVs and a BS are deployed to provide MEC services directly from UAVs or indirectly from the BS. We formulate an energy-efficient scheduling problem to minimize energy consumption by jointly optimizing UAV trajectories, task associations, and allocation of computing and transmitting resources. To solve it, this work designs a hybrid algorithm named _S_uccess History-based parameter Adaptation for Differential volution with a Niching-based population size reduction strategy and an efficient nsemble sinusoidal scheme (SHADE-NE). Experimental results validate the superiority of SHADE-NE over its benchmark peers, thus proving that SHADE-NE greatly enhances the performance of the system.

The advent of smart manufacturing in Industry 4.0 signifies the arrival of the era of connections. As an excellent communication protocol, Object linking and embedding for Process Control Unified Architecture (OPC UA) can address most semantic heterogeneity issues. However, its semantics are not formally defined at the application layer. To address the information silo problem caused by semantic heterogeneity, a method named Querying of Ontology Mapping-based OPC UA (QOMOU) is proposed. It extracts the information models of OPC UA servers into resource description framework triples, utilizes web ontology language for semantic enrichment and inference, and employs a semantic similarity model for event ontology mapping to improve query efficiency. The method's effectiveness is validated through functional queries using the SPARQL protocol in Apache Jena. The query efficiency is 5% higher on average compared to both structured query and extensible markup languages. Moreover, by employing a keyword-matching algorithm, the query accuracy of the existing heterogeneous data integration scheme is improved by 4% on average. This enhancement can boost the operational efficiency of Internet of Things systems based on the OPC UA architecture.

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions.

Multi-objective optimization algorithms are essential for addressing real-world challenges characterized by conflicting objectives. Although conventional algorithms are effective in exploring solution spaces and generating non-dominated solutions, solution quality and dynamic adaptability of true Pareto fronts need to be improved. This work proposes a multi-objective algorithm that integrates Non-dominated sorting genetic algorithm II (NSGA-II) and Multi-Objective Reinforcement Learning (N-MORL). N-MORL consists of two parts including upstream and downstream components. In the upstream component, this work improves the Variance-stabilized Multi-objective Proximal Policy Optimization (VMPPO) for enhanced convergence stability by adjusting its iteration mechanism. Additionally, this work optimizes variance networks and action sampling to balance exploration and exploitation, which improves experience sampling efficiency. This work adopts high-quality solution sets yielded by MORL as the initial solution set for downstream NSGA-II, guiding the exploration space and increasing the solution number. High-quality initial solutions significantly accelerate the iterative convergence speed of N-MORL. N-MORL provides the quality and the number of solutions, better covering or approaching the true Pareto front. Experimental results with five benchmark multi-objective functions demonstrate that N-MORL outperforms the other three multi-objective evolutionary algorithms regarding high-quality solutions with the same iterations.

Recent years have seen a surge in Internet of Things (IoT) technologies, with billions of mobile devices (MDs) straining limited computing and networking resources. Mobile edge computing offloads tasks from MDs to edge servers, saving energy and reducing network pressure. Edge servers provide closer services yet have fewer resources than cloud servers. A new heterogeneous edge and cloud computing paradigm combines the benefits of both. Edge servers provide close proximity services to MDs, while the cloud owns enough resources. The existence of mobile IoT devices makes it more practical to consider mobility when allocating resources of edge servers to decrease the energy consumption of the heterogeneous edge and cloud while meeting the latency needs of tasks. This work formulate a constrained energy consumption optimization problem and design a hybrid algorithm named Genetic Simulated-annealing-based particle swarm optimization (PSO) to yield a near-optimal solution. Simulation results prove that compared to genetic algorithm, PSO, simulated-annealing-based PSO, and Trex, GSPSO reduces the total energy consumption by 38.64%, 54.63%, 45.94%, and 36.21%, respectively.

In a cloud-edge system, data analysis, processing, and storage can be performed in edge servers, avoiding transferring data to more distant cloud servers. This greatly improves the efficiency of data processing, saves network bandwidth and cloud resources, and reduces operating and maintenance costs. However, it is a challenge of how to perform task scheduling. It is difficult to schedule tasks for joint optimization of the total energy consumption and completion time of a task sequence within a limited time in a resource-constrained cloud-edge system. The work proposes an improved Simulated-Annealing-based Firefly Algorithm with Linear position update, called SAFAL for short. SAFAL incorporates a simulated annealing mechanism and an efficient position update strategy into the firefly algorithm, enabling fireflies to find the optimal solution more quickly and avoid getting trapped in local optima. SAFAL adopts a probabilistic mapping operator to map the position of each firefly to a task scheduling sequence, thus linking the firefly space and the task space. Several test instances in cloud-edge systems are designed to validate the superiority of SAFAL over the firefly algorithm, simulated annealing, and firefly algorithm with a self-adaptive strategy. Results show that the weighted cost of total energy consumption and completion time of SAFAL is reduced by 16.32%, 17.62%, and 14.21%, respectively, with 20 tasks.

With the increasing amount of data and the need for real-time processing, Mobile Edge Computing (MEC) is growing rapidly, driving the shift from traditional cloud computing to distributed edge architectures. When offloading these applications with large amounts of data on mobile devices, a lot of computing and storage resources and high energy consumption are required. Yet, mobile devices' computing power, resource storage, and battery power are often limited and cannot meet these needs. To solve a computation offloading problem for joint optimization of time, cost, and energy, this work proposes an improved hybrid algorithm called Chaos and Lévy flights-based Whale Optimization Algorithm (CLWOA) to solve the multi-user offloading problem in an MEC-Cloud system. Each task is offloaded to local processors of mobile devices, edge servers, and cloud servers in proportion to jointly minimize the completion time, energy consumption, and total cost. Finally, compared with the whale optimization algorithm, lévy flight whale optimization algorithm, refined whale optimization algorithm, and chaos-based whale optimization algorithm, CLWOA reduces the weighted cost by 1.89%, 0.31%, 0.19%, and 0.42%, respectively.

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios.

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

Recently, many studies have used evolutionary algorithms (EAs) to optimize complex problems across various fields, including mechanical structure design, robotics, and cloud computing. EAs simulate the process of evolution to improve solutions to a given problem iteratively. However, EAs encounter significant challenges when dealing with high-dimensional expensive problems (HEPs). The large solution space and high computing cost of fitness evaluations (FEs) make optimization with limited FEs particularly difficult. To tackle this problem, an Adaptive Multi-stage Evolution Algorithm named AMEA is proposed. In AMEA, an adaptively enhanced teaching-learning-based optimization algorithm is adopted to explore the search space and find potential areas quickly. Then, in the next stage, the Gaussian process surrogate model and a genetic learning particle swarm optimization algorithm are adopted for further exploitation. Besides, this work proposes an adaptive stage switching criterion and an individual screening mechanism to enhance the optimization ability. AMEA demonstrates strong optimization performance when applied to HEPs. We compare AMEA with several state-of-the-art HEP optimization algorithms through seven benchmark functions, and the results show that it performs competitively with other algorithms. Finally, we also validate AMEA's effectiveness with a real-world computation offloading problem.

Resource usage prediction in cloud data centers is critically important. It can improve providers’ service quality and avoid resource wastage and insufficiency. However, the time series of resource usage in cloud environments is characterized by multidimensional, nonlinear, and high-volatility characteristics. Achieving high-accuracy prediction for time series with such characteristics is necessary but difficult. Traditional prediction methods based on regression algorithms and recurrent neural networks cannot effectively extract nonlinear features from data sets. Besides, many deep learning models suffer from gradient explosion or gradient vanishing during the training stage. Current commonly used prediction methods fail to uncover some vital information about the frequency domain features in the time series. To resolve these challenges, we design a Forecasting method based on the Integration of a Savitzky–Golay (SG) filter, a frequency enhanced decomposed transformer (FEDformer) model, and a frequency-enhanced channel attention mechanism (FECAM), named FISFA. It adopts the SG filter to reduce noise and smooth sequences in the raw sequences of resources. Then, we develop a hybrid transformer-based model integrating FEDformer and the FECAM, effectively capturing the frequency domain patterns. Besides, a meta-heuristic optimization algorithm, i.e., genetic simulated annealing-based particle swarm optimizer, is proposed to optimize key hyperparameters of FISFA. Then, FISFA predicts the future needs for multidimensional resources in highly fluctuating traces in real-life cloud environments. Experimental results demonstrate that FISFA achieves higher accuracy and performs more efficient prediction than several benchmark forecasting methods with realistic data sets collected from Alibaba and Google cluster traces. FISFA improves the prediction accuracy on average by 32.14%, 25.49%, and 27.71% over vanilla long short-term memory, transformer, and Informer methods, respectively.

Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming and advanced image editing. However, SMDs often face constraints in computational capacity and battery duration, restricting their ability to process these tasks instantaneously. Cloud computing can circumvent these limitations by computation offloading, but cloud data centers (CDCs) are often deployed at long distances from users, which results in longer computational latency. To address the latency issue, the incorporation of small base stations (SBSs) in the vicinity of the user provides services with high bandwidth and low latency. The primary challenge lies in balancing the economics of the system consisting of different SMDs, SBSs, and a CDC, i.e., minimizing cost while still meeting the latency requirements of applications. In this work, a cost-minimized computation offloading framework is formulated and solved by a two-stage optimization algorithm named Lévy flight and Simulated Annealing-based Grey wolf optimizer (LSAG). The optimal edge selection strategy is defined in the first stage for dealing with the case of several available SBSs. The second stage coordinates task scheduling and optimizes the allocation of resources among SMDs, SBSs, and CDC. LSAG integrates the extended search property of Lévy flight and the individual selection strategy of simulated annealing in the grey wolf optimizer, which reduces the risk of falling into local optima and finds the global optimum. Experimental results of executing real-life tasks show that LSAG outperforms its state-of-the-art peers in terms of cost and speed of convergence.

Precise and real-time prediction of future network attacks can not only prompt cloud infrastructures to fast respond and protect network security, but also prevents economic and business losses. In recent years, neural networks, e.g., Bi-directional Gated Recurrent Unit network and Temporal Convolutional Network (TCN), have been proven to be suitable for predicting time series data. Attention mechanisms are also widely used for the prediction of the time series of network attacks. This work proposes a hybrid deep learning prediction method that combines capabilities of Savitzky-Golay filter, TCN, Multi-head self attention, and Bi-directional Gated Recurrent Unit (STMB) for the prediction of network attacks. This work first adopts a Savitzky-Golay filter to smooth possible outliers and noise in network attack traffic data. It applies TCN to extract abstract features from one-dimensional time series to make full use of data. It then adopts multi-head self-attention to capture internal correlations among multi-dimensional features, by increasing weights of key features and reducing those weight of non-key features, making that SMTB captures important features adaptively. Finally, this work adopts Bi-directional Gated Recurrent Unit to extract bi-directional and long-term correlations in the time series to imporve the prediction accuracy. This work also utilizes a hybrid algorithm named genetic simulated-annealing-based particle swarm optimizer to determine the hyperparameter setting of STMB. Experimental results with real-life datasets show that STMB outperforms several commonly-used algorithms in terms of prediction accuracy.

In emerging applications of the Internet of Things, wireless sensor networks (WSNs) are often utilized to gather, track, and monitor data in remote areas with limited communication infrastructure. Since the majority of WSNs employ sensors powered by batteries, maintaining energy efficiency and conservation is crucial for ensuring their sustained operations over time. This work designs an Energy-efficient Unmanned aerial vehicle (UAV)-assisted hierarchical architecture of WSNs (EUW). EUW supports fast transmission of data collected from WSNs to a cloud server. Based on this architecture, this work first formulates a joint optimization problem for cluster head selection, time slot allocation, and UAV path planning to minimize the weighted sum of energy consumption of WSNs and that of a UAV. Then, a hybrid meta-heuristic algorithm named knowledge transfer-based particle swarm optimization (KTPSO) is designed, which utilizes previous optimization results to increase the convergence speed and find better results. Finally, numerical analysis and evaluation results are shown to demonstrate the efficiency of KTPSO and the proposed UAV-assisted architecture of hierarchical WSNs.

Accurate workload and resource prediction are critical to realizing proactive, dynamic, and self-adaptive resource allocation for building cost-effective, energy-efficient, and green cloud data centers (CDCs), providing satisfactory quality services to users and high revenue to cloud providers. However, it is challenging because patterns of dramatically increasing and large-scale workload and resource usage in CDCs vary significantly with time. Current prediction methods often fail to handle implicit noise data and capture nonlinear, long and short-term, and spatial characteristics in workload and resource time series, thus leading to limited prediction accuracy. To tackle these issues, this work designs a novel prediction approach named VSBG that seamlessly and innovatively combines Variational mode decomposition, Savitzky Golay, Bi-directional long short-term memory (LSTM), and Grid LSTM to predict workload and resource usage in CDCs accurately. VSBG innovatively integrates variational mode decomposition (VMD) and a Savitzky Golay (SG) filter in a four-step manner before exploring its prediction. VSBG leverages VMD to divide non-stationary workload and resource time series into multiple mode functions. Then, VSBG designs a quadratic penalty, solves it with a Lagrangian multiplier, and adopts a logarithmic operation and the SG filter to smooth the first mode function to eliminate noise interference. Finally, VSBG, for the first time, systematically and simultaneously captures depth and temporal characteristics of fluctuating and complex time series data with two BiLSTM layers, between which a GridLSTM layer lies, thereby accurately predicting workload and resources in CDCs. Extensive experiments with different real-world datasets prove that VSBG outperforms a holistic set of state-of-the-art algorithms on prediction accuracy and convergence speed.

Current Industrial Internet supports the sharing of information on heterogeneous resources and elements in a process of industrial production. It enables intelligent production processes and supports cost-effective scheduling. However, collaborative manufacturing and scheduling planning for enterprises with multiple plants cause several major challenges because of a large number of decision variables and constraints of manufacturing abilities of plants, resources of production, etc. Existing methods cannot comprehensively optimize the cost of multiple products in different plants, and fail to consider machine-level optimization of tasks of manufacturing. We propose a comprehensive machine-level architecture for enterprises with multiple plants. Based on this architecture, we formulate a limited non-linear integer optimization problem to decrease the total cost of transportation, production, and sales. In it, several real-life complicated nonlinear constraints are jointly considered, and they include constraints of storage space, replacement times, pairing production, substitution, and order fulfillment rates. To solve this optimization problem, we design a hybrid meta-heuristic optimization algorithm named Genetic Simulated annealing-based Particle Swarm Optimizer with Auto-Encoders (GSPAE). Extensive experiments with real-life data show that GSPAE decreases the total cost by 25% than other state-of-the-art methods.

Mobile edge computing (MEC) paradigm supports cloud-like computing capabilities at the edge of the network and offers low-latency services. Proxy servers of MEC with mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs) have emerged as competitors in providing services. This work considers a task offloading problem for an UAV-assisted MEC system and designs an integrated cloud-edge network with multiple mobile users (MUs) and layered UAVs to improve MEC with a network of UAVs. In our system, edge UAVs (EUAVs) and the cloud collaborate to provide caching and computing services for MUs. We consider static and dynamic applications that support task offloading. Our proposed approach minimizes the weighted cost of latency and energy consumption by jointly optimizing caching and offloading, deployment of EUAVs, and allocation of computation resources. Simultaneously, this work also considers UAVs’ caching and computation capacities while meeting MUs’ latency and energy constraints. Thus, a constrained mixed integer nonlinear program for a layered UAV-assisted hybrid cloud-edge system is formulated. To solve it, this work designs a hybrid metaheuristic algorithm named adaptive and genetic simulated annealing (SA)-based particle swarm optimization (AGSP). Experimental results with a real-life dataset verify that the AGSP’s system energy consumption and task latency are reduced by at least 7.4% and 8.46%, respectively, compared with the state-of-the-art algorithms, thus proving that AGSP greatly enhances the energy and latency of the system.

The rapid expansion of Internet users results in an immense influx of network traffic within extensive cloud data centers. Accurate and instantaneous identification and forecasting of network traffic aid system managers in efficiently distributing resources, assessing network performance based on specific service demands and scrutinizing the health of network status. However, sources and distributions of traffic are different, which makes accurate warnings of cyberattack traffic difficult. Recently, emerging neural networks have demonstrated their efficacy in forecasting time series data of network cyberattacks. The time series has temporal and spatial features, which can be efficiently captured with Informer and convolutional neural networks (CNNs). To realize high-performance spatiotemporal detection of cyberattacks, this work for the first time designs a hybrid and spatiotemporal prediction framework, which integrates CNNs, Informer, and a Softmax classifier to realize high-classification accuracy of normal and abnormal cyberattacks. Real-life data are adopted to evaluate the proposed method, which yields significant improvement in classification accuracy over typical benchmark classification models.

Hybrid cloud-edge systems combine the advantages of cloud computing and mobile edge computing (MEC) to achieve flexible integration and fluidity of data between the cloud and the edge. To address dynamic and stochastic loads caused by mobile users (MUs) and time-varying tasks, MEC network operators need to continuously migrate installed services among edge servers, significantly increasing network maintenance costs. Existing studies often overlook the service migration cost resulting from MU mobility. Therefore, we present a joint optimization scheme focusing on minimizing the operational cost of hybrid cloud-edge systems while considering the dynamic service migration cost induced by MUs. With the rapid development of 5G/6G technologies, many MUs require connectivity to edge nodes (ENs) or cloud data centers (CDCs) for processing. Minimizing the operational cost of hybrid cloud-edge systems while considering many heterogeneous decision variables is a challenge. To solve this complex high-dimensional mixed-integer nonlinear problem, we develop a novel deep learning-based evolutionary algorithm called autoencoder-based multiswarm gray wolf optimizer based on genetic learning (AMGG). Experimental results with real data demonstrate that AMGG achieves lower system cost by 49.69% while strictly meeting task latency requirements of MUs compared with state-of-the-art algorithms.

Driven by Service-Oriented Computing, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users’ dynamic preferences. Recently, users’ social connections have shown significant benefits to time-aware service recommendation, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm always presumes a given social network, which is not necessarily consistent with the similarities of service preferences among users and is expensive to collect for most service platforms. We propose a novel idea to learn the graph structure among historical mashups and make time-aware service recommendation for dynamic mashup creation collectively in a coupled framework. This idea raises two challenges, i.e., scalability and accuracy. To solve both challenges simultaneously, we introduce the Dynamic Relation Graph Learning (DRGL) framework for time-aware service recommendation. For scalability, our framework has a coarse-to-fine recalling strategy to learn the graph structure among the mashups, which enables the exploration of potential links among all historical mashups while maintaining a tractable amount of computation. For accuracy, we leverage recent advances in self-attention mechanisms to the mashup modeling and propose a transformer-based mashup encoder, which considers long-range dependencies in dense mashups for more accurate mashup representations. Extensive experiments show that the DRGL model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.

As the adoption of Service-Oriented Computing continues to grow, the number of web services has increased significantly, which makes service recommendation become an essential tool to assist users in selecting suitable services. However, a single service cannot satisfy the complex requirements of users, which has led to the emergence of a new technique known as Mashup, which combines services as reusable components to create value-added service compositions. Along with mashup, mashup recommendation has also become an indispensable and important component of service platforms. On service platforms, there are many heterogeneous entities and complex relationships between them. We divide these interaction into three different views: Mashup-Invocation view, Service-Consumption view, and Mashup-Composition view. As user preferences and characteristics of services and mashups are distributed across different views, their cooperation is crucial for accurate mashup recommendation. Therefore, we propose Cross-view Graph Alignment (CGA), a framework that captures the collaborative associations dispersed across different views and enhances the representation learning of users and mashups. This the first study to jointly tackle structure- and representation-level collaboration on the service platforms for better mashup recommendation. Experiments on two real-world service datasets show that CGA outperforms state-of-the-art methods and can better improve the mashup recommendation.

Current cloud data centers (CDCs) provide highly scalable, flexible, and cost-effective services to meet the performance needs of emerging applications. It is critical for CDC providers to predict future incoming workloads such that they can perform accurate resource provisioning in CDCs. Prediction accuracy is important and its improvement has been pursued in much existing work. This work adopts two different real-life Google data traces, based on which such prediction is conducted. Specifically, this work first gives a novel prediction mechanism that integrates wavelet decomposition, Savitzky–Golay (SG) filter, and autoregressive integrated moving average (ARIMA) to realize workload prediction in each time interval. The time series of the workload is smoothed with an SG filter and further divided into several components with wavelet decomposition. Then, an integrated approach is developed to predict statistical trends and their detail components. Real-life trace-driven experiments are done and the results suggest that the proposed method provides higher accuracy of prediction than its existing peers.

Evolutionary algorithms are commonly used to solve many complex optimization problems in such fields as robotics, industrial automation, and complex system design. Yet, their performance is limited when dealing with high-dimensional complex problems because they often require enormous computational resources to yield desired solutions, and they may easily trap into local optima. To solve this problem, this work proposes a Self-adaptive Teaching-learning-based Optimizer with an improved Radial basis function model and a sparse Autoencoder (STORA). In STORA, a Self-adaptive Teaching-learning-based Optimizer is designed to dynamically adjust parameters for balancing exploration and exploitation during its solution process. Then, a sparse autoencoder (SAE) is adopted as a dimension reduction method to compress search space into lower-dimensional one for more efficiently guiding population to converge towards global optima. Besides, an Improved Radial Basis Function model (IRBF) is designed as a surrogate model to balance training time and prediction accuracy. It is adopted to save computational resources for improving overall performance. In addition, a dynamic population allocation strategy is adopted to well integrate SAE and IRBF in STORA. We evaluate it by comparing it with several state-of-the-art algorithms through six benchmark functions. We further test it by applying it to solve a real-world computational offloading problem.

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in a high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search space. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a newly proposed Multi-swarm Gray-wolf-optimizer based on Genetic-learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that balance exploration and exploitation abilities, i.e., a dynamic subgroup number strategy for reducing the number of subpopulations, a subpopulation reorganization strategy for sharing useful information about each subpopulation, and a purposeful detection strategy for escaping from local optima and improving exploration ability. AMGG is compared with several widely used algorithms by solving benchmark problems and a real-life optimization one. The results well verify that AMGG outperforms its peers in terms of search accuracy and convergence efficiency.

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5.

Current mobile edge computing (MEC) owns cloud resources at the network edge, which enables low-latency mobile services. In addition to fixed MEC servers, MEC proxy servers with certain mobility and limited computing, e.g., flying unmanned aerial vehicles (UAVs), and vehicles, have emerged as competitors in providing services. In this work, aiming at a task offloading problem of a UAV-assisted MEC system, a hybrid network environment with multiple mobile devices (MDs) and multiple UAVs is established. A constrained mixed integer nonlinear program of the UAV-assisted hybrid cloud-edge system is formulated. A novel hybrid metaheuristic algorithm called Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) is presented to solve the program. Then, a task offloading and resource scheduling method is designed to intelligently minimize the total energy consumption of the hybrid system. Simulation results verify superiority of GSPSO over its three benchmark algorithms, thus demonstrating the proposed method significantly improves the energy efficiency of the UAV-enabled hybrid system.

Efficient and accurate identification of network anomalies is of great significance to the construction of network security systems in the information age. It is highly challenging to accurately detect abnormal behaviors in the increasing network data. Currently, classification methods based on feature extraction of autoencoders have been proven to be suitable for network anomaly detection. However, traditional detection models with autoencoders have poor detection accuracy in the face of massive network features. In addition, the hyperparameter optimization of their models cannot be effectively solved. For network anomaly detection, this work proposes a new network anomaly detection method named SAXP, which integrates Stacked sparse shrink Autoencoders and a XGBoost model based on genetic simulated annealing Particle swarm optimization (GSPSO). Specifically, features extracted by stacked sparse shrink autoencoders are introduced into the XG Boost model for classification, and GSPSO is used to optimize the hyperparameters of XGBoost. Experimental results based on two real-life data sets demonstrate that the proposed SAXP achieves higher recognition accuracy than several state-of-the-art algorithms.

Edge computing is a new architectural model that aims to offer computing, storage, and networking resources to support Internet of Things. Its primary strategy involves transferring computational tasks to the edge of network, which is closer to end-users. This paradigm facilitates offloading of computation, resulting in reduced latency and improved system performance. However, nodes located at the network edge have restricted energy and resources. As a result, running tasks entirely at the edge leads to higher energy consumption. This work proposes a novel three-tier offloading framework comprising of multiple mobile vehicles (MVs), a base station (BS), and a cloud data center (CDC). It jointly optimizes offloading rates of tasks, CPU computation rates of MVs, BS, and CDC, and the allocation of wireless bandwidth resources at MVs during partial computation offloading of tasks. It also considers limits of maximum computational resources and maximum delay of task execution. To further reduce the total system energy consumption, this work actively caches execution codes of tasks in MEC servers to reduce data transmission energy of MVs, which minimizes the total system energy consumption. This work develops a mixed integer nonlinear program and designs a mixed meta-heuristic algorithm with a multi-strategy adaptive particle swarm optimizer. Simulation results demonstrate that it outperforms various state-of-the-art algorithms by achieving lower energy consumption in fewer iterations.

Nowadays, smart mobile devices (SMDs) support various computation-intensive and delay-sensitive applications, e.g., online games, and figure compression. However, SMDs have limited computing resources and battery energy and cannot execute all tasks of the above applications in a real-time manner. Cloud computing provides enormous computing resources and energy that can easily execute tasks offloaded from SMDs. However, could data centers (CDCs) are often located in remote sites, which leads to long transmission time. Small base stations (SBSs) offer high-bandwidth and low-latency services for SMDs, which solves the problem of cloud computing. However, it becomes a challenge to achieve the lowest cost in such a heterogeneous architecture including multiple SMDs, SBSs, and the CDC while meeting delay requirements of tasks. This work proposes a cost-minimized computation offloading strategy to minimize the total cost of the system. A constrained optimization problem is first formulated based on the hybrid architecture. Afterward, a two-stage optimization algorithm called a Lévy flights and Simulated Annealing-based Grey wolf optimizer (LSAG) is developed to optimize the total cost of the system. In the first stage, the optimal edge selection policy is determined given multiple available SBSs. In the second stage, task offloading and resource allocation among SMDs, SBSs, and the cloud are determined. Experiments with real-life tasks prove that LSAG significantly achieves lower cost with faster convergence speed than state-of-the-art peers.

Fog computing provides lower latency and higher bandwidth compared to cloud computing and is widely used in Internet of Vehicles (IoV). Vehicles cannot compute all tasks locally due to their limited computing power and battery capacity. Thus, it is a useful way to offload some tasks of vehicles to other resource-rich servers. However, due to the high mobility of vehicles, there may be a failure of returning computing results. Thus, it is a challenge to minimize the latency of tasks while meeting the constraint of energy consumption. This work proposes a vehicle-fog offloading system that offloads tasks to fog servers or idle vehicles which is a probabilistic offloading problem. So this work proposes an improved optimization algorithm called an adaptive Lévy flight-based Whale optimization algorithm with Hierarchical learning (LWH) to solve this problem. Simulation experiments show that LWH has strong global search capability and outperforms its five typical and widely used algorithms.

Nowadays, rapid development of Internet has brought a sharp increase in traffic data. Abnormal traffic haS serious impact on network security. Traffic anomaly detection can be achieved by extracting characteristics of network traffic to detect anomalous intrusions, and therefore, anomaly detection algorithms are of great significance to maintenance of network security. This work proposes a hybrid spatio-temporal neural network with attention named CTGA to effectively identify anomalous traffic. CTGA combines a Convolutional neural network (CNN), a Temporal convolutional network (TCN), a bidirectional Gated recurrent unit network (BiGRU), and a self-Attention mechanism. It automatically extracts temporal and spatial features of sequences from raw data by sliding window preprocessing followed by CNN, TCN, BiGRU, and the self-attention mechanism to detect anomalous data. CNN is used to extract spatial features of time sequences and reduce the loss of spatial information. In the sequence, TCN obtains short-term features. Long-term dependencies in the data are captured by BiGRU, and the self-attention mechanism obtains important information in the sequence. Finally, experiments with the real-life Yahoo S5 dataset prove that CTGA outperforms other approaches substantially.

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system.

Cloud-edge hybrid systems are known to support delay-sensitive applications of contemporary industrial Internet of Things (IoT). While edge nodes (ENs) provide IoT users with real-time computing/network services in a pay-as-you-go manner, their resources incur cost. Thus, their profit maximization remains a core objective. With the rapid development of 5G network technologies, an enormous number of mobile devices (MDs) have been connected to ENs. As a result, how to maximize the profit of ENs has become increasingly more challenging since it involves massive heterogeneous decision variables about task allocation among MDs, ENs, and a cloud data center (CDC), as well as associations of MDs to proper ENs dynamically. To tackle such a challenge, this work adopts a divide-and-conquer strategy that models applications as multiple subtasks, each of which can be independently completed in MDs, ENs, and a CDC. A joint optimization problem is formulated on task offloading, task partitioning, and associations of users to ENs to maximize the profit of ENs. To solve this high-dimensional mixed-integer nonlinear program, a novel deep-learning algorithm is developed and named as a Genetic Simulated-annealing-based Particle-swarm-optimizer with Stacked Autoencoders (GSPSA). Real-life data-based experimental results demonstrate that GSPSA offers higher profit of ENs while strictly meeting latency needs of user tasks than state-of-the-art algorithms.

In the field of Web services computing, a recent demand trend is to mine user preferences based on user requirements when creating Web service compositions, in order to meet comprehensive and ever evolving user needs. Machine learning methods such as the latent Dirichlet allocation (LDA) have been applied for user preference mining. However, training a high-quality LDA model typically requires large amounts of data. With the prevalence of government regulations and laws and the enhancement of people’s awareness of privacy protection, the traditional way of collecting user data on a central server is no longer applicable. Therefore, it is necessary to design a privacy-preserving method to train an LDA model without massive collecting or leaking data. In this paper, we present novel federated LDA techniques to learn user preferences in the Web service ecosystem. On the basis of a user-level distributed LDA algorithm, we establish two federated LDA models in charge of two-layer training scenarios: a centralized synchronous federated LDA (CSFed-LDA) for synchronous scenarios and a decentralized asynchronous federated LDA (DAFed-LDA) for asynchronous ones. In the former CSFed-LDA model, an importance-based partially homomorphic encryption (IPHE) technique is developed to protect privacy in an efficient manner. In the latter DAFed-LDA model, blockchain technology is incorporated and a multi-channel-based authority control scheme (MCACS) is designed to enhance data security. Extensive experiments over a real-world dataset ProgrammableWeb.com have demonstrated the model performance, security assurance and training speed of our approach.

With the flourishing development of Everything-as-a-Service (EaaS) and Internet of Everything (IoE), Internet of Services (IoS) has recently emerged as a new buzzword in the field of service computing. Providing accurate and personalized service recommendations to users is essential yet highly challenging in IoS, from a sea of services. Besides the severe sparsity of users’ historical behavior data on services, little study has been reported in the literature on fully exploiting multiple relationship networks embedded in IoS. To fill this gap, we propose a novel Multi-Graph and Multi-Aspect neural network-powered method for Service Recommendation in IoS. Graph neural networks (GNNs) and attention mechanism are jointly employed to simultaneously extract information from a collection of heterogeneous knowledge graphs, constructed from historical data recorded in IoS including the user-service interaction graph, the user-user social graph, and the service-mashup graph. Based on the knowledge learned, user-service interactions are scrutinized from multiple aspects to better learn the multiple preferences of users and the multiple characteristics of services, in order to refine their profiles for future recommendation. The results of extensive experiments over the real-world datasets have demonstrated that MGMASR outperforms the baseline methods and can provide service recommendations more accurately for users in IoS.

Next location recommendation services play a pivotal role in Location-Based Social Networks (LBSNs) due to their ability to provide personalized recommendations of attractive destinations, resulting in substantial benefits for both users and service providers. Recent research indicates that these services are influenced by both sequential and geographical factors. However, we argue that most of these services fail to fully exploit the latent multi-group knowledge of location semantics and user preferences, resulting in suboptimal performance. Therefore, we propose STMGCL, a novel spatial-temporal multi-group contrastive learning-based method to discover intrinsic multi-group information for improving next location recommendation services. Specifically, STMGCL designs Spatial Group Contrastive Learning (SGCL) to extract multiple group knowledge regarding location semantics. Additionally, it develops Temporal Group Contrastive Learning (TGCL) to explore multiple user preference group information through a self-attention based encoder. Finally, we leverage a multi-task learning strategy and a generalized Expectation Maximization (EM) algorithm to ensure that STMGCL is optimized end-to-end with guaranteed convergence. Extensive experiments conducted on four real-world datasets demonstrate the superior performance of STMGCL over baselines.

Knowledge services are becoming a rising star in the family of XaaS (Everything as a Service). In recent years, people are more willing to search for answers and share their knowledge directly over the Internet, which makes the knowledge service ecosystem prosperous. In this paper, we aim to predict the popularity of knowledge services, which will benefit the downstream industries. Toward such a task, the spatial interactions (e.g., hyperlinks in Wikipedia) and temporal observations (e.g., page views) provide crucial information. However, it is difficult to utilize this information due to: (i) complicated and different usage observations, (ii) intricate and evolutionary spatial interactions, and (iii) small world trait of the network. To tackle such issues, we propose evolutionary graph convolutional recurrent neural networks (E-GCRNNs) to simultaneously model both temporal and spatial dependencies of knowledge services from their evolving networks. Additionally, a localized mini-batch training scheme is developed, which allows the E-GCRNNs to work on large-scale knowledge services network and reduce the prediction bias caused by the small world trait. Extensive experiments on real-world datasets have demonstrated that the proposed E-GCRNNs outperform baselines in terms of prediction accuracy, especially with the prediction range being longer, while remaining computationally efficient.

Driven by Service-Oriented Computing techniques, time-aware service recommendation aims to support personalized mashup development, adapting to the rapid shifts of users' dynamic preferences. Recent studies have revealed that users' social connections may help better model their dynamic preferences. However, two phenomena exist to influence users' dynamic preferences of service selection. First, users and their friends may only share preferences in certain services, which means not every service in the friends' consumed mashups has the same impact on a target user's dynamic preference. Second, for a target user, friends in his social network with similar interests and behaviors may contribute more influence intensities. To cover the above phenomena synergistically, this paper proposes a Social-powered Graph Hierarchical Attention Network (SGHAN), as a deep learning model capable of learning similar behaviors from proper friends during mashup development. SGHAN is powered by the reciprocity between its two core components: a service-level attentional encoder captures users' interested services in friends' mashups, while a friend-level graph attention network selects informative friends and propagates the friends' social influences. Extensive experiments show that the SGHAN model consistently outperforms the state-of-the-art methods in terms of prediction accuracy for mashup creation.

As an emerging concept, Knowledge as a Service (KaaS) aims to provide on-demand content-based (data, information, knowledge) delivery to meet the needs of users. With the prosperity of knowledge services, the prediction of the usage tendency of knowledge services has become an important and timely research topic. This study focuses on speculating the possible popularity of knowledge services in the next period of time, which can assist other downstream service tasks such as service recommendations. The interactions among knowledge services and their rich information (such as historical usage observation and text information) provide grounding for predicting the usage trend of services. However, recent spatial-temporal prediction based on graph neural networks usually depends heavily on the quality of manually created graphs, which may be expensive for knowledge services. To tackle such a limitation, this article proposes a novel Multi-modal Reciprocal SpatioTemporal (MRST) framework, which can jointly mine spatial dependencies and model time patterns for spatiotemporal coupling prediction. Two types of Edge Inference Networks (called EIN-o and EIN-t) are designed to sufficiently discover the spatial dependencies among knowledge services based on the data of usage observation sequences and service descriptions, respectively, and generate multi-modal directed weighted knowledge service graphs. Based on these graphs, MRST integrates GCN-based spatiotemporal prediction models as backbones to make predictions. Particularly, MRST features a unique reciprocal framework. On the one hand, EINs infer and generate multi-modal graphs to serve GCNs; on the other hand, GCNs utilize such spatial dependencies to make predictions and then introduce feedback to optimize EINs. In the meantime, to facilitate reproducible research, we collect a new knowledge service dataset fromWikipediacalled Wiki-EN dataset. Experiments on this real data set show that the proposed MRST framework significantly surpasses the baselines and can learn meaningful spatial dependencies outside the predefined graphic structure.

Currently, cloud computing service providers face big challenges in predicting large-scale workload and resource usage time series. Due to the difficulty in capturing nonlinear features, traditional forecasting methods usually fail to achieve high prediction performance for resource usage and workload sequences. Besides, there is much noise in original time series of resources and workloads. If these time series are not de-noised by smoothing algorithms, the prediction results can fail to meet the providers’ requirements. To do so, this work proposes a hybrid prediction model named VAMBiG that integratesVariational mode decomposition, anAdaptive Savitzky-Golay (SG) filter, aMulti-head attention mechanism,Bidirectional andGrid versions of Long and Short Term Memory (LSTM) networks. VAMBiG adopts a signal decomposition method named variational mode decomposition to decompose complex and non-linear original time series into low-frequency intrinsic mode functions. Then, it adopts an adaptive SG filter as a data pre-processing tool to eliminate noise and extreme points in such functions. Afterwards, it adopts bidirectional and grid LSTM networks to capture bidirectional features and dimension ones, respectively. Finally, it adopts a multi-head attention mechanism to explore importance of different data dimensions. VAMBiG aims to predict resource usage and workloads in highly variable traces in clouds. Extensive experimental results demonstrate that it achieves higher-accuracy prediction than several advanced prediction approaches with datasets from Google and Alibaba cluster traces.

As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, namedParticle swarm optimization based onGeneticLearning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs’ association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.

As increasingly more software services have been published onto the Internet, it becomes critical yet highly challenging to recommend suitable services to facilitate scientific workflow composition. This paper proposes a novel Natural Language Processing (NLP)-inspired approach to recommending services throughout a workflow development process, based on incrementally learning latent service representation from workflow provenance. A work-flow composition process is formalized as a step-wise, context-aware service selection procedure, which is mapped to next-word prediction in a natural language sentence generation. Historical service dependencies are extracted from workflow provenance to build and enrich a knowledge graph. Each path in the knowledge graph reflects a scenario in a data analytics experiment, which is analogous to a sentence in a conversation. All paths are thus formalized as composable service sequences and are mined, using various patterns, from the established knowledge graph to construct a corpus. Service embeddings are then learned by applying deep learning model from the NLP field. Extensive experiments on the real-world dataset demonstrate the effectiveness and efficiency of the approach.

As service-oriented architecture becoming one prevalent technique to rapidly compose functionalities to customers, increasingly more reusable software components have been published online in the form of web services. To create a mashup, however, it gets not only time-consuming but also error-prone for developers to find suitable services components from such a sea of services. Service discovery and recommendation has thus attracted significant momentum in both academia and industry. This paper proposes a novel incremental recommend-as-you-go approach to recommending next potential service based on the context of a mashup under construction, considering services that have been selected up to the current step as well as the mashup goal. The core technique is an algorithm of learning the embedding of services, which learns their past goal-driven context-aware decision making behaviors in addition to their semantic descriptions and co-occurrence history. A goal exclusionary negative sampling mechanism tailored for mashup development is also developed to improve training performance. Extensive experiments on a real-world dataset demonstrate the effectiveness of this approach.

Web service recommendation remains a highly demanding yet challenging task in the field of services computing. In recent years, researchers have started to employ side information comprised in a heterogeneous Web service ecosystem to address the issues of data sparsity and cold start in Web service recommendation. Some recent works have exploited the deep learning techniques to learn user/Web service representations accumulating information from multiplex sources. However, we argue that they still struggle to utilize multi-source information in a discriminating, unified and flexible manner. To tackle this problem, this paper presents a novel multi-source information graph-based Web service recommendation framework (MGASR), which can automatically and efficiently extract multifaceted knowledge from the heterogeneous Web service ecosystem. Specifically, different node-type and edge-type dependent parameters are designed to model corresponding types of objects (nodes) and relations (edges) in the Web service ecosystem. We then leverage graph neural networks (GNNs) with an attention mechanism to construct a multi-source information neural network (MIN) layer, for mining diverse significant dependencies among nodes. By stacking multiple MIN layers, each node can be characterized by a highly contextualized representation due to capturing high-order multi-source information. As such, MGASR can generate representations with rich semantic information toward supporting Web service recommendation tasks. Extensive experiments conducted over three real-world Web service datasets demonstrate the superior performance of our proposed MGASR as compared to various baseline methods.

Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky–Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy.Note to Practitioners—This work considers real-time and high-accuracy prediction of network traffic. It is highly important to well predict network traffic by capturing long-term dependence and effectively extracting high- and low-frequency information from time series data. Yet, it is a big challenge to achieve it because there are unstable characteristics and strong nonlinear features in the network traffic due to continuous expansion of network scale and fast emergence of new services. Current prediction methods usually have oversimplified theoretical assumptions, need significant time and memory, or suffer problems of gradient disappearance or early convergence. Thus, they fail to effectively capture the nonlinear characteristics of large-scale network sequences. This work proposes a hybrid prediction method named SG and TCN-based LSTM (ST-LSTM), which integrates the merits of the Savitzky–Golay filter, the temporal convolutional network (TCN), and the long short-term memory (LSTM), serving as smoothing time series, capturing short-term local features, and capturing long-term dependence, respectively. Experimental results based on the real-life dataset demonstrate that it achieves better prediction accuracy than its state-of-the-art peers, including the TCN and the LSTM. It can be readily implemented and deployed in many real-life industrial areas including smart city, edge computing, cloud computing, and data centers.

Driven by proliferation of the Service-Oriented Architecture (SOA), the quantity of published software services and their users keeps increasing rapidly in the service ecosystem; thus, personalized service selection and recommendation has remained a hot topic. Recent studies have revealed that users’ social connections may help better model their potential behaviors. Therefore, in this paper, we study how users’ high-order social networks may help improve service recommendation as well as its explainability. Two observations are set forth. First, a user’s service preference may be influenced by his trusted users, whom in turn influenced by their social connections. Second, such chained influences will not remain static and equally-weighted, as a user’s confidence over his social relations may vary confronted with different targeted services. We thus introduce a novel High-order Social Graph Neural Network (HSGNN) to support social-aware service recommendation. The key idea of the model is a graph convolution-based, multi-hop propagation module devised to extract the high-order social similarity signals from users’ local social networks, and encode them into the users’ general representations. Afterwards, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users’ specific preference. Extensive experiments in a real-world service dataset show that our HSGNN makes service recommendation more accurately, i.e., by 4.71% in terms of normalized discounted cumulative gain (NDCG), than state-of-the-art baseline methods.

The notion of collaborative scientific workflow is coined to address the increasing need for collaborative data analytics. In collaborative environments, access control policies are necessary for controlling the sharing of workflows, data products, and provenance information among collaborating parties. In particular, the protection of workflow provenance is critical because it often encodes the detailed protocol of a scientific experiment and carries the intellectual property of the respective stakeholders. In addition, since scientific workflows often evolve quickly, the corresponding access control policies for workflow provenance have to evolve as well. It is important to ensure that the evolution of workflow provenance access control policies maintain certain properties, in order to guarantee the correctness and performance of the corresponding policy enforcement. In this paper, we 1) propose a role-based access control model for scientific workflow provenance; 2) define three quality requirements for scientific workflow provenance access control policies - consistency, completeness, and conciseness; 3) develop a mechanism mapping from specifications of workflows to their counterparts in a provenance that preserves such quality properties, and 4) conduct a case study on a scientific workflow for autism behavioral data analysis that demonstrates the feasibility of our proposed analysis algorithms.

Driven by the widespread application of Service-Oriented Architecture (SOA), an increasing number of services and mashups have been developed and published onto the Internet in the past decades. With the number keeping on burgeoning, predicting the tendency of services invocation will provide various roles in service ecosystems with promising opportunities. However, services invocation bear three unique characteristics, which give rise to difficulties in predicting them. First, enormous services show different and complicated traits, like periodicity, nonlinearity and nonstationarity. Second, services providing similar or compensatory functions make up intricate relationship. Third, the combination dependencies between mashups and their comprising component services further amplify the difficulty. Given these factors, we have developed a tailored model Multi-Step Piecewise Recurrent Neural Network (MSP-RNN) to predict the tendency of services invocation. In MSP-RNN, Long Short Term Memory (LSTM) units are used to extract universal features. Based on these features, we have developed a piecewise regressive mechanism to make prediction discriminatingly. Besides, we have developed a multi-step prediction strategy to further enhance prediction accuracy and robustness. Extensive experiments in real-world data set with interpretable analysis show that MSP-RNN predicts the tendency of services invocation more accurately, i.e., by 3.7 percent in terms of symmetric mean absolute percentage error (SMAPE), than state-of-the-art baseline methods.

With the boom of Web services, there is a growing need for visualizing service ecosystems to help people browse services and understand their functionalities and positions in the systems. One foundational step of building a proper visualization is to ensure accurate representations for the comprising services. However, it is not a trivial task as service profiles may not be sufficient for two significant reasons. First, while the services themselves being used in various scenarios, their profiles may not always precisely reflect all of them. Second, service profiles usually comprise quite a few universal background terms that cannot distinguish services. To address these two issues, we apply machine learning techniques to incrementally learn service representations in a whole. A tailored topic model is developed, named Service Representation-Latent Dirichlet Allocation (SR-LDA). The core idea is to learn more comprehensive and up-to-date information about services from the profiles of the involved service compositions (i.e., mashup profiles), while introducing a global filter to identify and filter out background terms. Both quantitative and qualitative experiments on a real-world dataset demonstrate that the proposed SR-LDA builds higher-quality service representations comparing with baselines. We further generate a knowledge map to visualize a service ecosystem based on the learned service representations. Such a knowledge map directly leads to the detection of four typical functionality patterns of Web services and serves the purpose of mashup creation.

A growing number of organizations are hosting their software applications in distributed data centers (DCs) in the cloud, for faster response time and higher energy efficiency. The dramatic increase of user tasks, however, poses a significant challenge on DC providers to retain users’ expectations on both aspects. To tackle this challenge, this work first formulates the problem into a constrained biobjective optimization problem. A biobjective algorithm, named simulated-annealing-based adaptive differential evolution (SADE), is presented to simultaneously reduce both the response time of tasks and energy cost. Meanwhile, a method of minimal Manhattan distance is adopted to search for a final knee, for achieving a good balance between response time minimization and energy cost reduction. Experimental results on real-life datasets, i.e., the electricity prices and tasks collected from a Google cluster trace, have proved that SADE yields less task response time and lower energy cost compared with state-of-the-art algorithms.

Current large-scale green cloud data centers (GCDCs) tend to consume a huge amount of energy and generate enormous carbon emissions. Existing studies have tried to solve this problem by either realizing prediction of green energy, or optimizing task scheduling. In contrast, this work seamlessly combines green energy prediction and task scheduling to jointly optimize revenue and energy cost of GCDCs. Specifically, this work designs a prediction method, named Savitzky-Golay and Long Short-Term Memory network (SG-LSTM), to realize noise filtering and forecast green energy. Based on such prediction, a bi-objective optimization method, named Decomposition-based Multi-objective evolutionary algorithm with Gaussian mutation and Crowding distance (DMGC), is developed to optimize the revenue and energy cost of GCDCs. Its performance is demonstrated over real-life datasets including Google cluster traces, wind speeds, solar irradiance and prices of electricity. Experimental results show that SG-LSTM outperforms its two peers, back propagation neural network and gated recurrent unit, in terms of root mean square errors and mean absolute errors. In addition, DMGC surpasses its such peers as NSGA-II, SPEA2, and MOEA/D in terms of revenue, energy cost and average execution time. Particularly, DMGC's revenue is 18%, 20% and 13.1% higher, energy cost is 16%, 19.8% and 15.2% lower, and average execution time is 60.02%, 38.47% and 24.17% lower than those of NSGA-II, SPEA2, and MOEA/D, respectively.

Service recommendation for composition creation is a widely applied technique, which expedites mashup development by reusing existing services. The core of service recommendations is to simultaneously understand user needs as well as the functions of available services. However, the descriptions provided by users and service providers may not always be accurate or up to date, which poses significant challenges to composition creating. To tackle this problem, in this paper we propose a deep learning-based service recommendation framework named coACN, short for Collaborative Attention Convolutional Network, which can effectively learn the bilateral information toward service recommendation. On the one hand, a domain-level attention module is constructed to refine user needs embeddings by drawing messages from related service domains. On the other hand, a graph convolutional network is established to excavate the service-composition graph and fuse structured information into service embeddings. For a service node in the graph, the information of its compositions as its first-order neighbor nodes is used to supplement the latest functions and features of the service; and the information of the services as its second-order neighbor nodes may bring collaborative relationships into the service. Extensive experiments on the real-world ProgrammableWeb dataset show the significant improvement of our proposed coACN framework over state-of-the-art methods.

In recent years, Graph Convolutional Networks (GCNs) have been applied to benefit spatiotemporal predictions. The current shell for spatiotemporal predictions often relies heavily on the quality of handcraft, fixed graphical structures, however, we argue that such a paradigm could be expensive and sub-optimal in many applications. To raise the bar, this paper proposes to jointly mine the spatial dependencies and model temporal patterns in a coupled framework, i.e., to make spatiotemporal-coupled predictions. We come up with a novel Reciprocal SpatioTemporal (REST) framework, which introduces Edge Inference Networks (EINs) to couple with GCNs. From the temporal side to the spatial side, EINs infer spatial dependencies among time series vertices and generate multi-modal directed weighted graphs to serve GCNs. And from the temporal side to the spatial side, GCNs utilize these spatial dependencies to make predictions and then introduce feedback to optimize EINs. The REST framework is incrementally trained for higher performance of spatiotemporal prediction, powered by the reciprocity between its comprised two components from such an iterative joint learning process. Additionally, to maximize the power of the REST framework, we design a phased heuristic approach, which effectively stabilizes training procedure and prevents early-stop. Extensive experiments on two real-world datasets have demonstrated that the proposed REST framework significantly outperforms baselines, and can learn meaningful spatial dependencies beyond predefined graphical structures.

Existing Web service eco-systems are typically managed in a centralized manner, which hinders their further development due to inherent disadvantages such as trust issues, interest disputes, value separation and so on. The recently emerged blockchains provide distributed ledgers that enable parties who do not fully trust each other to maintain a set of global states, which provide a natural solution. Based on the INKchain, which is an open-source permissioned blockchain mechanism extending the Hyperledger Fabric, this paper proposes Trusted Decentralized Service Eco-System (T-DSES). T-DSES achieves not only fundamental functionalities of conventional systems, but also offers mechanisms to stimulate participants to bring trustworthiness to the whole system. The trustworthiness of T-DSES is realized by three strategies: reliable information of services and mashups, reliable records of participants’ rights, and reliable measurement of participants’ contributions. A customized token “SToken” is created to act as the media of value circulation. In this paper, the overall framework and detailed design of T-DSES are presented, especially including how to utilize Kubernetes to establish a cloud-based environment. A tailored Web front-end ensures the usability of operations. Over real-world data from ProgrammableWeb.com, analyses and experiments have been conducted to verify the feasibility and effectiveness of the presented approach.

With the widespread application of Service-Oriented Architecture, the quantity of web services keeps increasing rapidly over the Internet. Providing personalized service recommendation to users remains to be an important research topic. Recent studies have proved social connections helpful for modeling users' potential preference thus improving the performance of service recommendation. To date, however, one special type of social relation, called high-order social relation, has not been thoroughly studied. In reality, a user's preference may not only be affected by the user's direct neighbors, but also indirect ones. Furthermore, such influences may not remain static in the context of various attentions. To tackle such issues, we have developed a novel neural Attentive network based on High-order Social Graph (A-HSG) toward offering social-aware service recommendation. First, a graph convolution-based, multi-hop propagation module is devised to extract the high-order similarity signals from users' local social networks, and inject them into the users' general representations. Second, a neighbor-level attention module is constructed to adaptively select informative neighbors to model the users' specific preference. Extensive experiments over a real-life service dataset show that A-HSG outperforms baseline methods in terms of prediction accuracy.

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities.

A large number of services provided by cloud/edge computing systems have become the most important part of Internet services. In spite of their numerous benefits, cloud/edge providers face some challenging issues, e.g., inaccurate prediction of large-scale workload and resource usage traces. However, due to the complexity of cloud computing environments, workload and resource usage traces are highly-variable, thus making it difficult for traditional models to predict them accurately. Traditional models fail to deal with nonlinear characteristics and long-term memory dependencies. To solve this problem, this work proposes an integrated prediction method that combines Bi-directional and Grid Long Short-Term Memory network (BG-LSTM) models to predict workload and resource usage traces. In this method, workload and resource usage traces are first smoothed by a Savitzky-Golay filter to eliminate their extreme points and noise interference. Then, an integrated prediction model is established to achieve accurate prediction for highly-variable traces. Using real-world workload and resource usage traces from Google cloud data centers, we have conducted extensive experiments to show the effectiveness and adaptability of BG-LSTM for different traces. The performance results well demonstrate that BG-LSTM achieves better prediction results than some typical prediction methods for highly-variable real-world cloud systems.

Cloud computing is increasingly implemented by a growing number of organizations in recent years. Their critical business applications are deployed in distributed cloud data centers (CDCs) for fast response and low cost. The ever-increasing consumption of energy makes it highly important to schedule tasks efficiently in CDCs. In addition, many factors in CDCs, e.g., the wind and solar energy and prices of power grid have spatial differences. It becomes a challenging problem of how to achieve the energy cost minimization for CDCs in such a market. This work applies a G/G/1 queuing system to evaluate the optimization of servers in each CDC. Furthermore, a single-objective constrained optimization problem is given and addressed by a proposed Simulated-annealing-based Bees Algorithm to yield a close-to-optimal solution. Based on it, a Fine-grained Task Scheduling (FTS) algorithm is designed to minimize the energy cost of CDCs by intelligently scheduling heterogeneous tasks among distributed CDCs. In addition, it also determines running speeds of servers and the number of switched-on servers in each CDC while strictly meeting tasks' delay bounds. Realistic data-driven results demonstrate that FTS outperforms its typical benchmark scheduling peers in terms of energy cost and throughput.

As an emerging framework, edge computing achieves Internet of Things by providing computing, storage and network resources. It moves computation to edge devices located near users. Nevertheless, nodes in the edge often own limited resources and constrained energy capacities. It is impossible to entirely execute tasks in the edge due to their unsatisfied quality of service. Cloud data centers (CDCs) own almost unlimited resources yet they might cause large transmission delay and high resource cost. Consequently, it is highly needed to intelligently offload tasks between CDC and edge. This work proposes a task offloading algorithm for hybrid cloud-edge systems to achieve profit maximization of a system provider with response time bound assurance. It comprehensively investigates CPU, memory and bandwidth limits of nodes in the edge, and constraints of available energy and servers in CDC. These factors are integrated into a single-objective constrained optimization problem, which is solved by a simulated-annealing-based migrating birds optimization algorithm to yield a close-to-optimal offloading policy between CDC and the edge. Real-life data-driven experimental results show that its profit outperforms its four typical peers.

With the growing popularity of web services, more and more developers are composing multiple services into mashups. Developers show an increasing interest in non-popular services (i.e., long-tail ones), however, there are very scarce studies trying to address the long-tail web service recommendation problem. The major challenges for recommending long-tail services accurately include severe sparsity of historical usage data and unsatisfactory quality of description content. In this paper, we propose to build a deep learning framework to address these challenges and perform accurate long-tail recommendations. To tackle the problem of unsatisfactory quality of description content, we use stacked denoising autoencoders (SDAE) to perform feature extraction. Additionally, we impose the usage records in hot services as a regularization of the encoding output of SDAE, to provide feedback to content extraction. To address the sparsity of historical usage data, we learn the patterns of developers' preference instead of modeling individual services. Our experimental results on a real-world dataset demonstrate that, with such joint autoencoder based feature representation and content-usage learning framework, the proposed algorithm outperforms the state-of-the-art baselines significantly.

With the increasing popularity and great economic benefit from vulnerability exploitation, it is important to study mobile vulnerability in the mobile ecosystem. Beyond the traditional technical solutions such as developing technologies to identify potential vulnerabilities, discover the widely available exploitations and protect consumers from attacks, constructing the vulnerability market, a marketplace for vulnerability discovery, disclosure and exploitation, has been considered as an effective approach. Therefore, understanding the mechanism of the vulnerability market for further optimizations is attracting attentions from both academia and industry. Since mobile ecosystem is playing an increasingly important role for the daily life, this paper aims to understand the evolution of the mobile vulnerability market in a data-driven approach, aiming to identify the important issues for further research. Specially, a five-layer heterogeneous network, consisting of the software vendors, products, public disclosed vulnerabilities, hunters, organizations and their relations, is established to formally represent the evolution of the mobile vulnerability market. Based on the data collected from a variety of agencies, including NVD, OSVDB, BID and vendor advisories, a comprehensive empirical analysis is reported, focusing on the growth of the mobile vulnerability market as well as the interactions between mobile and other PCs platforms. Finally, suggestions drawn from the observations, including security evaluation for code reused, data leaking protection and permission overuse identification, hunter's strategy and behavior understanding, information sharing and external workforce hiring, as well as cross-platform vulnerability digging are discussed for further security enhancement.

Alternative, or non-traditional, data sources can be used to generate datasets which can in turn be analyzed for temporal, spatial and climatological patterns. Events and case studies inferred from the analysis of these patterns can be used by the remote sensing community to more effectively search for Earth observation data. In this paper, we present a new alternative Earth science dataset created from the National Weather Service's Area Forecast Discussion (AFD) documents. We then present an exploratory methodology for identifying interesting climatological patterns within the AFD data and a corresponding motivating example as to how these data and patterns can be used to search for relevant events or case studies.

Service ecosystems, consisting of various kinds of services and mashups, usually keep evolving over time.Existing works on the evolution of service ecosystems focus on either evaluating the impacts of single services' changes on the usage of services and the stability of the whole ecosystem, or discovering co-occurrence relationship between services, but fail to disclose any knowledge from the aspect of the evolution of service composition patterns.Based on our previous work, this paper moves one step further, revealing the latent service composition trends in a service ecosystem and providing more distinct explanation of different topic evolution patterns.A novel methodology, named Extended Dependency-Compensated Service Co-occurrence LDA (EDC-SeCo-LDA), is developed to calculate the directed dependencies between different topics and build topic evolution graph.The evolution trend

Service composition remains an important topic where recommendation is widely recognized as a core mechanism. Existing works on service recommendation typically examine either association rules from mashup-service usage records, or latent topics from service descriptions. This paper moves one step further, by studying latent topic models over service collaboration history. A concept of service co-occurrence topic is coined, equipped with a mechanism developed to construct service co-occurrence documents. The key idea is to treat each service as a document and its co-occurring services as the bag of words in that document. Four gauges are constructed to measure self-co-occurrence of a specific service. A theoretical approach, Service Co-occurrence LDA (SeCo-LDA), is developed to extract latent service co-occurrence topics, including representative services and words, temporal strength, and services' impact on topics. Such derived knowledge of topics will help to reveal the trend of service composition, understand collaboration behaviors among services and lead to better service recommendation. To verify the effectiveness and efficiency of our approach, experiments on a real-world data set were conducted. Compared with methods of Apriori, content matching based on service description, and LDA using mashup-service usage records, our experiments show that SeCo-LDA can recommend service composition more effectively, i.e., 5% better in terms of Mean Average Precision than baselines.

With the sweeping progress of service computing technology and crowdsourcing, individuals are offering their capability as human services online. Companies are orchestrating human services for complex problem-solving, resulting in the rapid growth of human service ecosystems nowadays. Considering the unique characteristics of human services, like capability growth and human-involving collaboration, it is essential to understand the patterns of the development and collaboration among human services. Therefore, this paper proposes a three-layer time-aware heterogeneous network model to quantify the evolution in the human service ecosystem. Based on the model, an exploratory empirical study is presented to uncover how human service providers and consumers develop their capability in service provision and orchestration, as well as how human services collaborate with each other over time. Insights from the emerging patterns open a gateway for further research to facilitate human service adoption, including human service composition recommendation, human skill expansion suggestion, and systematic mechanism design.

Semantic annotations play an important role in semantics-aware service discovery, recommendation and composition. While existing approaches and tools focus on facilitating the development of semantic annotations on web services, the validation of the quality of annotations is largely overlooked. Meanwhile, the refinement of semantic annotations mostly goes through manual processes, which not only is time-consuming but also requires significant domain knowledge. To enhance the Quality of Semantic Annotation (QoSA), we have developed a technique to incrementally assess and correct semantic annotations of web services. Aiming at supporting web service interoperation, we have formalized the QoSA of input and output parameters. Based on such formalism, test cases are automatically generated to validate service annotations. Learned semantic instances are then accumulated to iteratively validate semantic annotations of other services. Furthermore, a three-phase optimization methodology including local-feedback, global-feedback, and global-propagate is developed to improve the QoSA by incrementally correcting inaccurate annotations. Experiments over a real-world web services repository have demonstrated that our technique can effectively improve QoSA of services, gaining a 78.68 percent improvement in input parameters annotations and identifying 36.47 percent inaccurate output parameters annotations. The proposed technique can be equipped at various service repositories to enhance service discovery and recommendation.

A growing number of global companies select Green Cloud Data Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. This work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed cloud data centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.

Existing service ecosystems typically rely on some centralized service registries (e.g., ProgrammableWeb.com) as "middle people" to record service behaviors thus to provide service ranking and recommendation. Excessive centralization increasingly becomes the bottleneck and hinders the further growth of the service ecosystems. As the first attempt to apply the fundamental technique underneath the emerging Bitcoin network into the field of service oriented computing, this paper proposes to build a service ecosystem as a decentralized blockchain-oriented service network, called Decentralized Service Eco-System (DSES). Whenever any activity occurs in the system (e.g., APIs are used together in a published mashup), all involved parties will individually store and maintain a copy of the detailed record (provenance) at their local databases. Such a distributed database-oriented solution will enable services who do not fully trust each other to maintain a set of global states. In this way, service discovery and recommendation can be realized in a distributed manner that promises higher scalability and maintainability. As a proof of concept, a prototyping system of DSES is constructed using the real-world data from ProgrammableWeb.com, based on the INKchain, a newly open-source consortium blockchain mechanism extending the Hyperledger Fabric.

Driven by the widespread application of Service-Oriented Architecture (SOA), the quantity of web services and their users keeps increasing in the service ecosystem. Since services are hosted by service providers, it will be very helpful to predict the tendency of services invocation for service providers, so that proper actions may be taken to ensure the quality of services. Two major challenges exist in predicting the tendency of services invocation, however. First, different service invocation sequences may bear different and complicated characteristics, which is hard to be modeled generally. Second, the intricate relations between service invocation sequences are valuable but hard to be discriminated and utilized. To address these issues, a deep neural network, named Piecewise Recurrent Neural Network (PRNN), is developed by taking both generality and pertinence into consideration. For generality, PRNN extracts complicated characteristics of all service invocation sequences through Long Short-Term Memory (LSTM) units. For pertinence, PRNN develops a piecewise mechanism, through which service invocation sequences can be clustered automatically and predicted discriminatingly. Extensive experiments in real-world dataset show that PRNN outperforms baseline methods in predicting the tendency of services invocation.

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. The Data Science and Informatics Group (DSIG) at the NASA Marshall Space Flight Center (MSFC), has been using deep learning for a variety of Earth science applications. This paper provides examples of the applications and also addresses some of the challenges that have been encountered.

Web services are self-contained software components that support business process automation over the Internet, and mashup is a popular technique that creates value-added service compositions to fulfill complicated business requirements. For mashup developers, looking for desired component services from a sea of service candidates is often challenging. Therefore, web service recommendation has become a highly demanding technique. Traditional approaches, however, mostly rely on static and potentially subjectively described texts offered by service providers. In this paper, we propose a novel way of dynamically reconstructing objective service profiles based on mashup descriptions, which carry historical information of how services are used in mashups. Our key idea is to leverage mashup descriptions and structures to discover important word features of services and bridge the vocabulary gap between mashup developers and service providers. Specifically, we jointly model mashup descriptions and component service using author topic model in order to reconstruct service profiles. Exploiting word features derived from the reconstructed service profiles, a new service recommendation algorithm is developed. Experiments over a real-world data set from ProgrammableWeb.com demonstrate that our proposed service recommendation algorithm is effective and outperforms the state-of-the-art methods.

The four papers in this special section provide deep research results to report the advance in mobile and cloud computing services. In recent years, cloud computing has become a scalable services consumption and delivery platform in the field of Services Computing. The technical foundations of cloud computing include Service-Oriented Architecture (SOA) and virtualizations of hardware and software. The goal of cloud computing is to share resources among the cloud service consumers, the cloud service providers, and the cloud vendors in the cloud value chain.

Nowadays many companies and organizations choose to deploy their applications in data centers to leverage resource sharing. The increase in tasks of multiple applications, however, makes it challenging for a data center provider to maximize its revenue by intelligently scheduling tasks in software-defined networking (SDN)-enabled data centers. Existing SDN controllers only reduce network latency while ignoring virtual machine (VM) latency, thus may lead to revenue loss. In the context of SDN-enabled data centers, this paper presents a workload-aware revenue maximization (WARM) approach to maximize the revenue from a data center provider's perspective. The core idea is to jointly consider the optimal combination of VMs and routing paths for tasks of each application. Comparing with state-of-the-art methods, the experimental results show that WARM yields the best schedules that not only increase the revenue but also reduce the round-trip time of tasks of all applications.

Early intervention in autism, although deemed as essential, has high variance in the outcome attained, partially due to complex interaction between multitude of factors and variables involved, and the lack of systematic study to untangle their influences in the outcome. Therefore, pairing set of interventions with an individual children to cater for their need remains highly challenging. From the perspective of parents, unknown factors emanate from their unfamiliarity with what interventions are out there and why. From the perspective of caregivers, it is critical to understand unique attributes of the individual children develop over time. There is a scarcity of exploration of interactions between attributes specific to a child, family characteristics and therapeutic, medical and educational services. In this research, we aim to bridge the gap. In this study, we identify predictive features pertaining to each individual child and how they interact responding to different interventions and services. We have studied temporal data and model improvement/regression outcomes at different timestamped milestones and overlayed a model to aid parents and caregivers in coming up with pragmatic intervention plan. We propose a scientific workflow to automate the modeling process and rely on DATAVIEW to guarantee computational reproducibility and data fidelity. We use data collected by SFARI dataset for evaluation. To the best of our knowledge, this is first-time amalgamation between the Autism Health informatics community and the Workflow community; and this is the first-time study that combines prediction methods applied on Autism Spectrum Disorder (ASD) Phenotype data to provide guidance to parents and caregivers.

Service (API) discovery and recommendation is key to the wide spread of service oriented architecture and service oriented software engineering. Service recommendation typically relies on service linkage prediction calculated by the semantic distances (or similarities) among services based on their collection of inherent attributes. Given a specific context (mashup goal), however, different attributes may contribute differently to a service linkage. In this paper, instead of training a model for all attributes as a whole, a novel approach is presented to simultaneously train separate models for individual attributes. Meanwhile, a latent attribute modeling method is developed to reveal context-aware attribute distribution. Experiments over real-world datasets have demonstrated that this fine-grained method yields higher link prediction accuracy.

Workflow is an important way to mashup reusable software services to create value-added data analytics services. Workflow provenance is core to understand how services and workflows behaved in the past, which knowledge can be used to provide a better recommendation. Existing workflow provenance management systems handle various types of provenance separately. A typical data science exploration scenario, however, calls for an integrated view of provenance and seamless transition among different types of provenance. In this paper, a graph-based, uniform provenance model is proposed to link together design-time and run-time provenance, by combining retrospective provenance, prospective provenance, and evolution provenance. Such a unified provenance model will not only facilitate workflow mining and exploration, but also facilitate workflow interoperability. The model is formalized into colored Petri nets for verification and monitoring management. A SQL-like query language is developed, which supports basic queries, recursive queries, and cross-provenance queries. To verify the effectiveness of our model, A web-based, collaborative workflow prototyping system is developed as a proof-of-concept. Experiments have been conducted to evaluate the effectiveness of the proposed SQL-like graph query against SQL query.

With the rapidly increasing number of services, there is an urgent demand for service recommendation algorithms that help to automatically create mashups. However, most traditional recommendation algorithms rely on the original service descriptions given by service providers. It is detrimental to the recommendation performance because original service descriptions often lack comprehensiveness and pertinence in describing possible application scenarios, let alone the possible language gap existing between service providers and mashup developers. To solve the above issues, a novel method of Targeted Reconstructing Service Descriptions (TRSD) for a specific mashup query is proposed, resorting to the valuable information hidden in mashup descriptions. TRSD aims at introducing mashup descriptions into service descriptions by analyzing the similarity between existing mashups and the specific query, while leveraging service system structure information. Benefit from this approach, missing application scenarios in original service descriptions, query-specific application scenario information, mashup developers' language habits, and service system structure information are all integrated into the reconstructed service descriptions. Based on the reconstructed service description by TRSD, a new service recommendation strategy is developed. Comprehensive experiments on the real-world data set from ProgrammableWeb.com show that the overall MAP of the proposed TRSD model is 6.5% better than the state-of-the-art methods.

Service recommendation plays a critical role in fostering the growth of service ecosystems. However, existing methods are mainly in favor of a small number of popular services while newly emerged ones (i.e., newborn services) are largely ignored, which hurts the systems in two aspects. First, the potential of many services, especially the newborn ones, is wasted. Second, service ecosystems highly depending on a few kernel services are not diversified nor robust. To address this issue, we propose to proactively recommend collaborative services for newborn ones. The aim is to illuminate how to use the newborn services and fertilize their proper usages. While this is a cold start problem, frequent collaboration among newborn or dissimilar services makes it more difficult. In this situation, a Divide-and-Conquer approach is adopted utilizing category tags and collaboration records (DCCC). For each newborn service, the approach first produces one ranked list of old services and one list of newborn services, separately. DCCC then merges the two lists into one for recommendation. Experiments over a real-world dataset from ProgrammableWeb demonstrate that the proposed approach achieves significant improvement in recommendation accuracy compared with baseline methods.

A key factor of win–win cloud economy is how to trade off between the application performance from customers and the profit of cloud providers. Current researches on cloud resource allocation do not sufficiently address the issues of minimizing energy cost and maximizing revenue for various applications running in virtualized cloud data centers (VCDCs). This paper presents a new approach to optimize the profit of VCDC based on the service-level agreements (SLAs) between service providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines at different service classes. An analytic probabilistic model is then developed for non-steady VCDC states. In addition, a smart controller is developed for fine-grained resource provisioning and sharing among multiple applications. Furthermore, a novel dynamic hybrid metaheuristic algorithm is developed for the formulated profit maximization problem, based on simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. The advantage of the proposed approach is validated with trace-driven simulations.

With the booming number of web services, it is a challenge for inexperienced developers to select suitable services and make service compositions. Therefore, recommending services based on user queries becomes a necessity. For modeling the queries and services' descriptions, many recent studies are based on LDA (Latent Dirichlet Allocation). However, some previous empirical works indicate that LDA model doesn't gain high accuracy in generating latent presentation which is subject to the restrictive assumption of the Dirichlet-Multinomial distribution. In this paper, we propose a Time-aware Collaborative Poisson Factorization (TCPF) to tackle the problem. TCPF takes Poisson Factorization as the foundation to model mashup queries and service descriptions separately, and incorporate them with the historical usage data together using collective matrix factorization. Experiments on the real-world ProgrammableWeb dataset show that our model outperforms the state-of-the-art methods (e.g., Time-aware collaborative domain regression) by 7.7% in terms of mean average precision, and costs much less time on the sparse, massive and long-tailed data set.

Service ecosystem consists of all kinds of services, and some of them may be composed by developers to create new mashups. Existing work on service recommendation and composition mine either frequent patterns from mashup-service usage records, or latent topics from service metadata. In this paper, we propose Service Co-occurrence LDA (SeCo-LDA), a novel approach that mines latent topic models over service co-occurrence patterns. The key idea is to treat each service as a document, and its bag of co-occurring services as the bag of words in that document. Using this model, we can analyze such service co-occurrence documents with a probabilistic topic model. We show how to derive service co-occurrence topics, and then validate our model on the real-world ProgrammableWeb.com dataset. We illustrate that SeCo-LDA can discover meaningful latent service composition patterns including their temporal strength and services' impacts, which conventional Apriori can not reveal. Comparing with Apriori, content matching based on service description and LDA directly using mashup-service usage records, we have demonstrated that SeCo-LDA can recommend service composition more effectively, 5% better in terms of MAP than the baseline approach.

In the era of Big Data, data analysis gives strong competition power to enterprises. As services for Big Data Analysis (BDA) become prevalent, analysis services with intelligence and autonomy using automatic service composition show very bright prospects in the BDA market. Service composition consists of four stages: workflow generation, discovery, selection, and execution. In this paper, we propose a novel service discovery approach that considers two key concerns in the discovery domain towards better quality as well as effective service composition. BDA services are fine grained according to the domain and functional behaviors. The services need a domain context-aware and precision-guided discovery approach. Therefore, we propose domain ontology-based service discovery. It is mainly focused on the BDA domain for precise service discovery considering all behavioral signatures between queries and services. As for the second concern, components in composed services depend greatly on each other in situations such as workflow for data analysis. We show that linking services together considering sociability or user preference gives better discovery performance. We propose a Linked Social Service Network (LSSN) with multiple feature attribute-based service discovery for BDA. Our approach combines two advantages, the precision and sociability of Web services. The experimental results show that both of these methods perform well based on their perspectives, better than previous approaches.

As more and more reusable web services are published on the Internet, how to help users quickly identify appropriate candidate services has become an increasingly critical challenge. Most of the current research efforts on service discovery rely on syntax and semantics-based service matchmaking. In contrast, this paper presents a novel way of applying network routing mechanism to facilitate service discovery, featuring scalability and performance. Services annotated by Web Ontology Language for Services (OWL-S) are organized into a network based on semantic clustering. Virtual routers are created representing clusters, and Bloom Filters are generated for service routing. A service search request is thus transformed into a network routing problem to quickly locate semantic service cluster and in turn to candidate services. In addition, the deterministic annealing technique is applied to facilitate service classification in the network construction. Dynamic network adjustment is operated to ensure the search performance in the network. Empirical study over common testbed annotated in OWL-S is reported.

The balance between customer-perceived application performance and cloud provider's profit is a key to achieve win-win in cloud economy. Current researches on cloud resource allocation do not sufficiently address the issue of minimizing energy cost and maximizing revenue for various applications in virtualized cloud data center (VCDC). This paper presents a new approach to realize the optimization of VCDC's profit based on the service-level agreements between cloud providers and customers. A precise model of the external and internal request arrival rates is proposed for virtual machines of different service classes. An analytic probabilistic model is then developed for non-equilibrium VCDC states. Next, a smart controller is proposed for fine-grained resource provisioning and sharing among multiple applications. A novel hybrid meta-heuristic algorithm based on simulated annealing and particle swarm optimization is developed to solve the formulated profit maximization problem. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Finally, the effectiveness of the proposed approach is validated with trace-driven simulation.

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowdsourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

Semantic annotation plays an important role for semantic-aware web service discovery, recommendation and composition. In recent years, many approaches and tools have emerged to assist in semantic annotation creation and analysis. However, the Quality of Semantic Annotation (QoSA) is largely overlooked despite of its significant impact on the effectiveness of semantic-aware solutions. Moreover, improving the QoSA is time-consuming and requires significant domain knowledge. Therefore, how to verify and improve the QoSA has become a critical issue for semantic web services. In order to facilitate this process, this paper presents a novel lifecycle framework aiming at QoSA assessment and optimization. The QoSA is formally defined as the success rate of web service invocations, associated with a verification framework. Based on a local instance repository constructed from the execution information of the invocations, a two-layer optimization method including a local-feedback strategy and a global-feedback one is proposed to improve the QoSA. Experiments on real-world web services show that our framework can gain 65.95%~148.16% improvement in QoSA, compared with the original annotation without optimization.

Big Data analytics provide support for decision making by discovering patterns and other useful information from large set of data. Organizations utilizing advanced analytics techniques to gain real value from Big Data will grow faster than their competitors and seize new opportunities. Cross-Industry Standard Process for Data Mining (CRISP-DM) is an industry-proven way to build predictive analytics models across the enterprise. However, the manual process in CRISP-DM hinders faster decision making on real-time application for efficient data analysis. In this paper, we present an approach to automate the process using Automatic Service Composition (ASC). Focusing on the planning stage of ASC, we propose an ontology-based workflow generation method to automate the CRISP-DM process. Ontology and rules are designed to infer workflow for data analytics process according to the properties of the datasets as well as user needs. Empirical study of our prototyping system has proved the efficiency of our workflow generation method.

Existing scientific workflow tools, created by computer scientists, require that domain scientists meticulously design their multi-step experiments before analyzing data. However, this is oftentimes contradictory to a domain scientist's routine of conducting research and exploration. This paper presents a novel way to resolve this dispute, in the context of service-oriented science. After scrutinizing how Earth scientists conduct data analytics research in their daily work, a provenance model is developed to record their activities. Reverse-engineering the provenance, a technology is developed to automatically generate workflows for scientists to review and revise, supported by a Petri nets-based workflow verification instrument. In addition, dataset is proposed to be treated as first-class citizen to drive the knowledge sharing and recommendation. A data-centric repository infrastructure is established to catch richer provenance to further facilitate collaboration in the science community. In this way, we aim to revolutionize computer-supported Earth science.

Mashup has emeraged as a promising way to allow developers to compose existed APIs (services) to create new or value-added services. With the rapid increasing number of services published on the Internet, service recommendation for automatic mashup creation gains a lot of momentum. Since mashup inherently requires services with different functions, the recommendation result should contain services from various categories. However, most existing recommendation approaches only rank all candidate services in a single list, which has two deficiencies. First, ranking services without considering to which categories they belong may lead to meaningless service ranking and affect the recommendation accuracy. Second, mashup developers are not always clear about which service categories they need and services in which categories cooperate better for mashup creation. Without explicitly recommending which service categories are relevant for mashup creation, it remains difficult for mashup developers to select proper services in a mixed ranking list, which lower the user friendliness of recommendation. To overcome these deficiencies, a novel category-aware service clustering and distributed recommending method is proposed for automatic mashup creation. First, a Kmeans variant(vKmeans) method based on topic model Latent Dirichlet Allocation is introduced for enhancing service categorization and providing a basis for recommendation. Second, on top of vKmeans, a service category relevance ranking (SCRR) model, which combines machine learning and collaborative filtering, is developed to decompose mashup requirements and explicitly predict relevant service categories. Finally, a category-aware distributed service recommendation (CDSR) model, which is based on a distributed machine learning framework, is developed for predicting service ranking order within each category. Experiments on a real-world dataset have proved that the proposed approach not only gains significant improvement at precision rate but also enhances the diversity of recommendation results.

In the paradigm of service oriented science, scientific computing applications and data are all wrapped as web accessible services. Scientific workflows further integrate these services to answer complex research questions. However, our earlier study conducted on myExperiment has revealed that although the sharing of service-based capabilities opens a gateway to resource reuse, in practice, the degree of reuse is very low. This finding has motivated us to propose ServiceMap to provide navigation facility through the network of services to facilitate the design and development of scientific workflows. This paper proposes ReputationNet as an enhancement of ServiceMap, to incorporate the often-ignored reputation aspects of services/workflows and their publishers, in order to offer better service and workflow recommendations. We have developed a novel model to reflect the reputation of e-Science services/workflows, and developed heuristic algorithms to provide service recommendations based on reputations. Experiments on myExperiment have illustrated a strong positive correlation (with Pearson correlation coefficient 0.82) between the reputation scores computed and the actual performance (i.e. usage frequency) of the services/workflows, which demonstrates the effectiveness of our approach.

Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, service ecosystem is evolving over time with services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of service ecosystem on service recommendation. This paper employs a probabilistic approach to predict the popularity of services to enhance the recommendation performance. A method is presented that extracts service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware service recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-service usage in an evolving service ecosystem. Experiments on a real-world service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information.

Service compositions inherently require multiple services each with its domain-specific functionality. Therefore, how to mine matching patterns between services in relevant domains and compositions becomes crucial to service recommendation for composition. Existing methods usually overlook domain relevance and domain-specific matching patterns, which restrict the quality of recommendations. In this paper, a novel approach is proposed to offer domain-aware service recommendation. First, a K Nearest Neighbor variant (vKNN) based on topic model Latent Dirichlet Allocation (LDA) is introduced to cluster services into semantically coherent domains. On top of service domain clustering results by vKNN, a probabilistic matching model Domain Router (DR) based on Extreme Learning Machine (ELM) is developed for decomposing a requirement to relevant domains. Finally, a comprehensive Domain Topic Matching (DTM) model is built to mine relevant domain-specific matching patterns to facilitate service recommendation. Experiments on a large-scale real-world dataset show that DTM not only gains significant improvement at precision rate but also enhances the diversity of results.

Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques such as keyword search and semantic matching while others are based on Quality of Service (QoS) prediction. However, services and their mashups are evolving over time with publishing, perishing and changing of interfaces. Therefore, a practical service recommendation approach should take into account the evolution of a service ecosystem. In this paper, we present a method to extract service evolution patterns by exploiting Latent Dirichlet Allocation (LDA) and time series prediction. A time-aware service recommendation framework for mashup creation is presented combing service evolution, collaborative filtering and content matching. Experiments on real-world ProgrammableWeb data set show that our approach leads to a higher precision than traditional collaborative filtering and content matching methods.

Modern scientific data management and analysis usually rely on multiple scientists with diverse expertise. In recent years, such a collaborative effort is often structured and automated by a data flow-oriented process called scientific workflow. However, such workflows may have to be designed and revised among multiple scientists over a long time period. Existing workbenches are single user-oriented and do not support scientific workflow application development in a "collaborative fashion". In this paper, we report our research on the enabling techniques in the aspects of collaboration provenance management and reproduciability. Based on a scientific collaboration ontology, we propose a service-oriented collaboration model supported by a set of composable collaboration primitives and patterns. The collaboration protocols are then applied to support effective concurrency control in the process of collaborative workflow composition. We also report the design and development of Confucius, a service-oriented collaborative scientific workflow composition tool that extends an open-source, single-user development environment.

NASA Earth Exchange (NEX) is a collaboration platform whose goal is to accelerate Earth science research, by leveraging NASA's vast collections of global satellite data together with access to NASA's High-End Computing (HEC) facilities. NEX also aims to facilitate the sharing of experimental results as well as scientific processes (workflows) with the Earth science community through integration with VisTrails workflow management system. While VisTrails is used internally, it is not easily accessible from remote computers without directly logging into the NASA HEC systems through twofactor authentication and a bastion host. This paper describes the initial design of an extensible architecture that facilitates easier workflow interaction on NEX, by enabling users to develop and execute workflows in a supercomputing environment directly from their local VisTrails installation. This architecture helps domain scientists seamlessly leverage distributed computing and storage resources and it is potentially applicable to other scientific workflow management software. We further describe the architecture of the VisTrails-HEC plugin (as well as the VisTrails-Amazon plugin) and the implementation of a working prototype to demonstrate the feasibility of our solution.

As one foundational technology of cloud computing, services computing is playing a critical role to enable provisioning of software as a service (SaaS). However, how to effectively and efficiently discover proper available services from the cloud of resources remains a big challenge. This paper reports our continuous efforts on semantic services discovery. We extend the Support Vector Machine (SVM)-based text clustering technique in the context of service-oriented categorization in a service repository, and propose an iterative process to incrementally enrich domain ontology. A popular Web 2.0 mashup platform is used as a testbed; and preliminary evaluation results are reported.

caGrid has accumulated a repository of biomedical services, however, how a cancer researcher can find proper services in the caGrid when needed remains a big challenge. This research aims to enhance the cyber infrastructure of caGrid, by developing a mechanism that turns caGrid services into semantic-aware interoperable services. We proposed a service semantics model, and developed a technique that automatically extracts semantic metadata from static WSDL service descriptions. Such semantic information is stored as loosely coupled annotations that can be queried using semantic Web techniques, to enhance services discovery and composition. We also proposed a two-phase discovery technique that helps users quickly identify interested service operations. This paper also reports our examinations over available techniques and recommends a feasible infrastructure for biomedical service reuse. A prototyping system is developed as a proof of concept.

Large-scale scientific data management and analysis usually relies on many distributed scientists with diverse expertise. In recent years, such a collaborative effort is often composed and automated into a dataflow-oriented process, a so-called scientific workflow. However, existing scientific workflow tools are single user-oriented and do not support collaborative scientific workflow composition, execution, and management among multiple distributed scientists. In this paper, we report our study of collaboration protocols towards building a tool supporting collaborative scientific workflow composition. Based on a scientific collaboration ontology, we propose a collaboration model supported by a set of collaboration primitives and patterns. The collaboration protocols are then applied to support effective concurrency control in the process of collaborative workflow composition.

Services Computing has become an increasingly important area in the IT and business sectors. In particular, Services now account for more than half of the economy in the United States and other countries. Numerous Services Computing-related degree programs and accreditation processes are being created. However, very few systematic guidelines exist for building graduate programs for Services Computing. In this paper, we present a reference model of the Masters Program in Services Computing for academic institutions and accreditation agencies as a relevant curriculum guideline. Specifically, the core and elective courses are introduced to help build the reference program. The inter-connections between core and elective courses are also illustrated to help create concentration programs based on the introducing sequences of the courses. Some practices of delivering Services Computing related courses and conducting accreditation application process are presented in this paper to help others more rapidly initiate the adoption process of the Services Computing curriculum.

Service oriented architecture (SOA) is one central technical foundation supporting the rapidly emerging cloud computing paradigm. To date, however, its application practice is not always successful. One major reason is the lack of a systematic engineering process and tool supported by reusable architectural artifacts. Toward this ultimate goal, this paper proposes a variation oriented analysis method of performing architectural building blocks (ABB)-based SOA solution design for enabling cloud application design. We present the modeling of solution-level architectural artifacts and their relationships, whose formalization enables event-based variation notification and propagation analysis. We report a prototype tool and describe how we extend the Unified Modeling Language (UML) mechanism to implement the system and enable solution-level variation analysis and enforcement in business cloud as an example.

In recent years, a number of scientific workflow management systems (SWFMSs) have been developed to help domain scientists synergistically integrate distributed computations, datasets, and analysis tools to enable and accelerate scientific discoveries. As more scientific research projects become collaborative in nature, there is a compelling need of dedicated services to support collaborative scientific workflows on the Internet. This paper reviews the state of the art of the field of scientific workflows towards the support of collaborative scientific workflows, identifies critical research challenges, and presents our ongoing research work aiming to study how to create services supporting collaborative scientific workflows.

This paper presents our design and development of a context-driven content adaptation planner, which dynamically transforms requested Web content into a proper format conforming to receiving contexts (e.g., access condition, network connection, and receiving device). Aiming to establish a semantic foundation for content adaptation, we apply description logics (DLs) to formally define context profiles and requirements and automate content adaptation decision. In addition, the computational overhead caused by content adaptation can be moderately decreased through the reduction of the size of adapted content.

This paper reports several key challenges and solutions when we apply Web 2.0 mashup technology to build a university-oriented services portal. A two-layer mashup service model is proposed as the underlying basis to support multiple granularities of services mashup. We explore a caching technique to facilitate personalizable services requests. We also report our preliminary practice of exploiting Facebook as a social relationship data source.

In the mobile Internet, users mostly work with handheld devices with limited computing power and small screens. Their access conditions also change more frequently. In this paper, we present a novel service supporting intelligent content adaptation to better suit handheld devices. The underlying technique is a unit of information (UOI)-based content adaptation method, which automatically detects semantic relationships among comprising components in Web contents, and then reorganizes page layout to fit handheld devices based on identified Ious. Experimental results demonstrate that our method enables more exquisite content adaptation.

The rapidly emerging technology of Web services paves a new cost-effective way of engineering software to quickly develop and deploy Web applications by dynamically integrating other independently developed Web-service components to conduct new business transactions. This paper reports our efforts on designing and developing a Web service of pass-through authentication (PTA) for 12 online electronic-payment Web applications. In accordance with how a PTA service is developed and integrated with a corresponding back-end e-payment system, our strategies can be categorized in three stages: end-to-end integration stage, Web-services-enabled stage, and Web-services-oriented stage. Derived from real-world industrial experience, this three-stage pathway can be applied to a broad range of Web-application development projects to guide smooth transformation from a specific application-oriented design and development model toward a reusable Web-services-oriented model. Furthermore, this paper contributes to an engineering process that leads to practical Web-services-oriented software development. New research issues revealed by this project are also reported.

As Web services become more prevalent, the need to ensure their quality increases. This paper explores the criteria of reliability of Web services-oriented systems, and discusses how to design and generate test cases to conduct tests over Web services. A prototype system is constructed to test the effectiveness and efficiency of our algorithms. The preliminary results show that our approach facilitates the testing of services-oriented systems.

Formal electronic conferences (FEC) refers to online meetings for a geographically distributed group of people that are regulated by a rigorous set of rules. FEC technologies enable organizations to replace face-to-face business meetings with trustworthy virtual online meetings. In This work we present a Robert's rules of order (RRO)-compatible, motion-driven discussion-thread-centered meta-model, which is capable of uniformly modeling formal electronic conference activities. A tailored computerized mechanism, the collaboration description language (CODL) and its runtime environment, is also developed to formalize the model. The CODL virtual machine adds a layer of encapsulation that decouples FEC applications from underlying platforms: therefore, the development of FEC applications will become more reliable, efficient, and secure. Our preliminary experience with this meta-model is also reported.

The emerging paradigm of Web services opens a new way of Web application design and development to quickly develop and deploy Web applications by integrating independently published Web services components to conduct new business transactions. As research aiming at facilitating Web services integration and verification, WS-Net is an executable architectural description language incorporating the semantics of colored Petri-net with the style and understandability of object-oriented concepts. WS-Net describes each Web services component in three layers: interface net declares the services that the component provides to other components; interconnection net specifies the services that the component acquires to accomplish its mission; and interoperation net describes the internal operational behaviors of the component. As an architectural model that formalizes the architectural topology and behaviors of each Web services component as well as the entire system, WS-Net facilitates the verification and monitoring of Web services integration.

The paradigm of Web services that transforms the Internet from a repository of data into a repository of services has been gathering significant momentum in both academia and industry in recent years. However, as more and more Web services are published on the Internet, how to choose the most appropriate Web service components from the sea of ever-changing, unpredictable, and largely uncontrollable Web services poses a big challenge. we propose a mobile agent-based approach that selects reliable Web service components in a cost-effective manner.

Electronic meeting control has been the focus of many research efforts; however, extensively adopted standards are still unavailable. In this paper, we present a generic model for electronic meeting control. Our model is based on the formal meeting protocol commonly known as the parliamentary procedure or Robert's Rules of Order (RRO). Capturing the essential features of traditional RRO, we suggest extension mechanisms in order to exploit the capabilities of electronic media. Centered on the concept of a discussion thread, our extended RRO model can handle concurrent meeting activities, multiple floors, amendment nesting, and discussion hierarchies. M-Net, an electronic meeting system, is introduced as an example supported by our model.

Web application development differs from the development of traditional software in several significant ways; therefore requirements engineering for Web applications entails new demands accordingly. This paper proposes an extreme Web requirements engineering - mockup-driven fast-prototyping methodology to help elicit and finalize system requirements, as well as facilitate adjustment to quickly changing user requirements typical to Web applications. Supporting the inclusion of customer feedback early in the development process, this strategy minimizes the risk of wasting valuable development efforts because of ambiguous or incomplete specifications. Real-life experiences of the use of the methodology in industry are reported as examples.

A Web service is a software application published on the Web and accessible through standard Internet protocols. Multimedia Web services generally integrates multimedia contents over Web services. Core techniques of web services need to be enhanced accordingly in order to facilitate multimedia transportation and handling. Seamlessly integrated with simple object access protocol (SOAP), this paper presents a model for network router supporting dynamic selection and binding of most efficient protocol serving for quality of service (QoS) based multimedia Web services.

Computer supported cooperative work (CSCW) research is the discipline that explores how to utilize computing and networking technologies to facilitate cooperation and collaboration among people. A variety of research has been conducted on CSCW architecture. However, how to ensure CSCW system effectiveness and efficiency on supporting collaboration remains a challenge. In this paper we present a rule mitigated framework for CSCW applications. This research contributes to the state of the art by presenting a robust and comprehensive architecture to support distributed collaboration.

Abstract Web application development can be very complicated without an appropriate framework, architecture and application model. A good implementation model can help application developers communicate with clients, consolidate the design before starting the development, speed up the development, and make the code highly reusable. This paper proposes a mockup‐driven fast prototyping methodology (MODFM) for the development of Web applications. It is built on the most recent Web technologies: EJB, JSP, Servlet, XML, Struts, and Web application server. A two‐tier Model‐View‐Controller (MVC) architecture is proposed as the underlying backbone and a supporting environment is tailored specifically in order to enable development. Two basic supporting tools are provided: the dynamic menu generator and the generic code generator, which produce code for front‐end, back‐end and database schemas. MODFM helps to generate fully functional mockup systems for the client to review at an early analysis stage, and continues to provide guidance throughout follow‐on development phases. Real‐life experiences on the use of this methodology in industry are presented as examples. Copyright © 2003 John Wiley & Sons, Ltd.